A 3-year fully-funded PhD position is available through the Alzheimer's Society scheme in UK Dementia Research Institute at the University of Cambridge.

The project aims to identify the components of the intrinsic cellular machineries for handling aggregation-prone proteins, and characterise circumstances in which these machineries are activated, with the view to identifying new therapeutic targets to ameliorate/prevent the aggregation load in neuronal cells (associated with neurodegenerative diseases). The project stems from in-house developed technology enabling detection of failures in the protein-folding quality control system with high space-time resolution, and its ability to visualise the existence of the aggregation-resolving activity in live cells.

Student will develop the experimental models for monitoring protein quality control and aggregation in live iPSC-derived neurons, and analyse the activity of the intrinsic systems applying a range of biochemical, molecular biology and advanced light imaging techniques. Along with molecular biology/biochemistry, the work will involve training in light microscopy techniques such as Fluorescence Lifetime Imaging, super-resolution microscopy and single particle tracking using state of the art optics and software. This work will be conducted with support of interdisciplinary collaborators including physicists and computer scientists, enabling optical tools, mathematical modeling and data analysis.

More info

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PhD candidate within Faculty of Sociology, Anthropology and Folklore, School of Social Sciences, University of Iceland


We are looking for applicants for a full-time position for a Ph.D. student in sociology at the Faculty of Sociology, Anthropology and Folklore. The funding is for three years, but each hiring period is one year according to requirements about the progress of the project.


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Field of work
The project focuses on social inequality and requires analysis of quantitative data and writing peer-reviewed articles in English. The project is a part of an international study of inequality (International Social Survey Programme, ISSP), and consists of data from surveys collected from the public in about 40 countries that measure people´s attitudes and experiences in regard to inequality. In addition, there is a possibility of collecting and analyzing qualitative data with a specific focus on Iceland.

Qualification requirements
Applicants must have completed an M.A.-degree and to fulfill the requirement of entry to doctoral studies at the Faculty of Sociology, Anthropology, and Folklore, or to be current doctoral students at the Faculty
Good writing skills in English
Experience with quantitative methods
Experience with qualitative methods is a plus
Good interactional skills
Ability to be initiative and to work independently

More info

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Doctoral Grant by the University Research Fund (BOF) in the area of Administrative Law

This doctoral research project aims to identify general principles of proof in Belgian administrative law. This is an aspect of general administrative law on which, despite its importance, a general theory based on fundamental research is lacking in Belgium. The project requires a thorough study of the case law of the Belgian administrative courts and involves comparative law too. The researcher will be embedded in the research group Government & Law (https://www.uantwerpen.be/en/research-groups/government-and-law/).

Job denoscription

You prepare a doctoral thesis on the basis of a project ennoscriptd ‘Towards a Theory on General Principles of Proof in Administrative Law’.
You publish scientific articles related to the research project of the assignment.
You contribute to teaching and research in the Faculty of Law, research group Government & Law.

More info

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Doctoral Grant (BOF), Multimorbidity and its cost in the Belgium health system - 2019BAPDOCPROEX275
The Faculty of Medicine and Health Sciences is seeking to fill a full-time (100%) position for a

Doctoral Grant by the University Research Fund (BOF) in the area of Multimorbidity and its cost in the Belgium health system

Multimorbidity is a growing phenomenon, but research has only recently taken off. How patients with multimorbidity use health care is not well known, despite its implications on quality and cost. This PhD aims to improve understanding of the burden, management and cost of multimorbidity in the Belgium health system.

The Department of Primary and interdisciplinary care and the Department of Social Epidemiology and Health Policy lead this doctoral research project. It is part of the new spearhead research on quality of integrated care.

More info

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PhD Student positions in Engineering Psychology with focus on Clinical Research
Lulea
Ref 5010-2019

The research subject Engineering Psychology is looking for a PhD student within the newly established Research School in the field of Humans and Technology at Luleå University of Technology. As a doctoral student in the Research School, you will become part of the research environment in the Human and Technology division with over 60 employees. You will be placed in the Engineering Psychology subject group and you will also be a part of the Research School along with doctoral students in Human Work Science, Industrial Design, and Product Innovation. The Research School will commence during the 2019/2020 academic year.

Research in Engineering Psychology is concerned primarily with the interaction between humans and the natural and social environment. Psychology is an exciting subject that can be applied in most situations where people are involved, which means that the research areas studied all play an important role in society and the business community. We offer our doctoral students a dynamic, international research environment in close collaboration with industries and leading universities around the world.

More info

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PhD student position in Political Science
Lulea
Ref 5014-2019

We are looking for a PhD students in political Science within a new Research School at Luleå University of Technology’s Social Sciences Division. The Division includes around 50 employees in the areas of Political science, Economics, History and Jurisprudence. The student will be placed in the Political Science group and be part of a newly established Research School.

The Research School will start during the 2019/2020 academic year. Its overarching theme is how society can realize the global Sustainable Development Goals (UN 2030 Agenda for Sustainable Development), which will require new forms of collaboration, new business models, new technologies, changes in legislation, reassessed norms, and different societal policies and policy instruments. Changes like these will create winners and losers and produce important goal and value conflicts.

Research at the Social Sciences Division is oriented towards natural resources, environmental and energy issues and frequently takes place in a multidisciplinary environment where academic subjects from the Faculty of Science and Technology also participate. We strive for an international perspective in our education and research.

More info

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PhD student positions in Entrepreneurship and Innovation
Lulea
Ref 5019-2019

The research subject Entrepreneurship and Innovation is seeking a PhD student within the framework of a newly started research school in Industrial Engineering and Management at Luleå University of Technology. As a doctoral student in the research school, you become part of the research environment within the Industrial Engineering and Management department with about sixty employees. You are placed within the subject group for Entrepreneurship and Innovation and are part of the graduate school together with doctoral students in Accounting and control, Quality Technology and Logistics and Industrial Marketing. The graduate school will start during the fall 2019 and spring 2020.

Entrepreneurship and innovation at Luleå University of Technology was established in 2006 and today has about 20 employees with a broad competence base. Our goal is to be an international center for the development and dissemination of future ideas in entrepreneurship and innovation. Entrepreneurship and innovation include organizational development with a particular focus on business development based on innovative products, services and production solutions, as well as ways of managing and organizing businesses. The research group collaborates with large national and international companies, small and medium-sized enterprises (SMEs), policy makers and other research groups. Research results are produced in close collaboration with industry and other community actors. To strengthen our research in business model innovation, we are now seeking a PhD student.

More info

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2-year Research Grant in Augmented Reality for Art and Cultural Heritage

he Signals and Images Laboratory of ISTI-CNR (National Research Council of Italy) is looking for possible candidates for a two-year research grant (28,000 EURO per year). The official announcement is imminent.
The research topics will focus on Augmented Reality applied to art and cultural heritage.

Requirements: MSc in computer science (or computer engineering), age less than 36 years.
Required knowledge: mobile platforms development (Android, iOS), knowledge of Unity 3D.

For further information, please contact massimo.magrini@isti.cnr.it

Thank you and best regards,
Davide Moroni

--
Davide Moroni, PhD
Signals & Images LAB
Institute of Information Science and Technologies

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Seeking a Studentship Position in AI-ML-NLP-DataScience areas in Computer Science

Dear Researchers, Professors and colleagues,

Greetings! I trust you are doing great.

I am Partha Pratim Saha, a Senior Data Scientist, looking for an Integrated Master's cum PhD studentship position across the globe in the areas of Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing or in related areas of Computer Science.

I would expect a scholarship as I am planning to start this position as soon as possible. Please let me know if you and/or any of your colleagues are interested in taking a student in the similar research group.

Thanking you. I look forward hearing from you soon.

With Sincerely,
Partha Pratim Saha
Email: technical.partha@gmail.com


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Postdoctoral and PhD positions on Natural Language Processing / Information Extraction, INESC TEC / University of Porto.
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The Artificial Intelligence Lab (LIAAD) of INESC TEC / U. Porto is (https://www.inesctec.pt/en/centres/liaad) is looking for one highly motivated post doc and two MSc holders to join the team of researchers working on the Text2Story project. The topic is Narrative Extraction from Text. The aims of the project are to develop Machine Learning and Information Extraction algorithms and tools for Identifying, formally representing and reusing narrative structures from textual sources.

The selected candidates will join the Machine Learning and NLP team of LIAAD-INESC TEC (FCUP or INESC TEC) and will have the opportunity to work in an exciting and young environment in close interaction with research engineers, PhD students and post-doctoral researchers who are working on varied aspects that concern Machine Learning, Information Extraction and Computer Science.

The ideal candidates will have a PhD (or MSc) in NLP or equivalent and the following skills:

* Strong motivation to work with NLP from algorithms to demos and tools;
* Theoretical background and hands-on experience on NLP, Machine Learning, Information Retrieval and Information Extraction;
* Good communication skills and ability to cooperate within a team;
* Good knowledge of programming languages such as, Python, R and Java;
* Knowledge of Python and development tools (e.g. GIT);
* Being able to use Portuguese as a language of study is important.

For the postdoc mostly:
* Motivation to co-coordinate work-packages, co-supervise students and help with project management;
* Motivation to articulate with other projects on the topic and go after funding for the research line;

This positions are supported by the FCT project Text2Story. FCT is the Portuguese National Science Funding Agency.

For more information and applications:

Postdoc: https://www.inesctec.pt/en/opportunities/natural-language-processing-machine-learning-AE2019-0343
MSc: https://www.inesctec.pt/en/opportunities/machine-learning-AE2019-0342

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University of York, PhD in Mechanistic Biology
Project noscript: The circadian clock in the ageing plant
https://www.findaphd.com/phds/project/the-circadian-clock-in-the-ageing-plant/?p112907

Project Denoscription
Plants respond to environmental signals in a time-of-day dependent manner,
but we do not yet know how this process is affected by age. In the long term,
we would like to predict how changes in weather throughout a growth season
will influence a plant, so farmers can plan their harvests even under
unpredictable weather patterns caused by climate change.

This PhD project will investigate how the genetic targets of the circadian clock
change as plants age, with a focus on environmental signalling and
developmental genes. We are looking for a candidate that wants to develop
into a well-rounded computational and experimental scientist. For instance, a
great candidate could be a statistics or computer science graduate who wants
some exposure to experimental work or a biology student who wishes to focus
their PhDs on bioinformatics and statistics.

From a ‘data science’ perspective, the project is interesting because it will
include ‘time series’ of ‘time series’ across different time scales
(developmental time series of circadian time series). From a biological
perspective, it focusses on a fundamental question: how is circadian
regulation of gene expression affected by ageing?

ML aspects of project: supervised and unsupervised learning on multi-scale longitudinal data sets

Funding Notes
This is a BBSRC White Rose DTP studentship fully funded for four years and covers: (i) a tax-free annual stipend at the standard Research Council rate (£15,009 estimated for 2020 entry), (ii) research costs, and (iii) tuition fees at the UK/EU rate.

References
Entry requirements: Students with, or expecting to gain, at least an upper second class honours degree, or equivalent, are invited to apply. The interdisciplinary nature of this research project means that we welcome applications from students with backgrounds in any biological, chemical, and/or physical science, or students with mathematical backgrounds who are interested in using their skills in addressing biological questions

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Postdoc position in Adaptive Online Learning and Learning Theory

Dear all,

I have an opening for a postdoc position in my group to work with me on Adaptive Online Learning and other topics in Statistical Learning Theory.

The ideal starting date would be the fall of 2020 or early 2021.

A requirement is to have multiple theory papers at COLT/NeurIPS/ICML/JMLR or similar.

If you are interested and are attending NeurIPS next week, then please contact me to arrange to meet up informally at the conference.
To apply, send me your CV and a short denoscription of your background at tim@timvanerven.nl.

Best regards,
Tim

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Principal NLP Research Scientist at ACTNext

Overview
ACT is a nonprofit organization helping people achieve educational and workplace success. Our programs are designed to boost lifelong learning in schools and workplaces around the world. Whether it's guiding students along their learning paths, enabling companies to develop their workforce, fostering parent, teacher, and counselor understanding of student progress, guiding job seekers toward career success, or informing policymakers about education and workforce issues. ACT is passionate about making a difference in all we do. Learn more about working at ACT at act.org.

Are you passionate about solving big scientific problems in education

through innovative technology and working in a highly multi-disciplinary collaborative environment? Would you like to know more about how computer vision, affective computing and machine learning are helping create new forms of learning and assessment tools? If this sounds intriguing, then we'd like to talk to you about a role as part of a new ACTNext ACT R&D team tackling a set of problems requiring significant innovation.

More info

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Master research internship, Paris: Cross-lingual transfer with multi-lingual BERT via linguistically informed fine-tuning

Title: Cross-lingual transfer with multi-lingual BERT via linguistically informed fine-tuning
- Duration: 5-6 months, during the year 2020
- Location: LIMSI, Orsay (south of Paris)
- Supervisor: Caio Corro - http://caio-corro.fr/
- Team: Spoken Language Processing / Traitement Automatique de la Parole
- Contact: caio.corro@limsi.fr

Context

Recently, much attention has been paid to large scale pre-training of context-sensitive representations (or context-sensitive word embeddings), in particular ELMO [1] and BERT [2] models. The main idea is to pre-train the first layers of a neural network on a large amount of unlabeled data before fine-tuning the rest of the network on a downstream task. As such, context-sensitive representations allow to lower annotation cost and improve classification performance on a wide range of tasks.

The multilingual BERT model pre-trains context sensitive representations on a collection of texts in 104 languages instead of texts in a single language. One question that arises is whether we can use the multilingual BERT model for cross-lingual learning,
that is training a model on a subset of these languages (source languages) and testing it on a different subset (target languages). This problem is both important under a research perspective (how can we learn multi-lingual representations of typologically diverse languages?) and under an applied industry perspective (i.e. increase language coverage of NLP-based products at low cost). Previous work observed that cross-lingual transfert based on multi-lingual BERT works best for typological similar languages (i.e. languages with similar word order), which is expected but disappointing [3].

This internship will focus on multilingual dependency parsing with the Universal Dependency treebank https://universaldependencies.org/ . Previous work has considered re-ordering source language sentences with respect to word order in target languages [4]. However, re-ordering is not possible for unsupervised large scale pre-training where syntactic structures is not annotated. A different line of work proposed to force word order statistics at test time using constraints [5], but this method is based on a costly lagrangian optimization procedure and cannot be applied on a per sentence basis. Alternatively, we propose to explore fine-tuning methods for multi-lingual BERT model using a linguistically informed training algorithm, i.e. to use dominant word order information (is the object placed before or after the verb in a given language?) to ensure unsupervised transfer to target languages.
Missions

The successful candidate will develop neural network architectures and training algorithms for cross-lingual generalization of pre-trained context-sensitive representations. The main evaluation task will be cross-lingual dependency parsing. As there are many ways to tackle this problem, the specific approach will be determined by the intern aspiration, which could be for example posterior regularization or latent variable modeling. In a nutshell, the aim is to:
- propose a method for cross-lingual generalization of multi-lingual BERT using typological information;
- evaluate the proposed method on cross-lingual parsing;
- evaluate if results generalize to other tasks, for example cross-lingual named entity recognition.

[1] "Deep Contextualized Word Representations" Matthew Peters et al.
[2] "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" Jacob Devlin et al.
[3] "How multilingual is Multilingual BERT?" Telmo Pires et al.
[4] "Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge" Lauriane Aufrant et al.
[5] "Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing" Tao Meng et al.

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Postdoc Fellowship at the National Institutes of Health - Machine Learning for Computer-Aided Diagnosis and Medical Image Analysis

Hello:



A post-doctoral fellowship is available for 2D/2.5D/3D/4D radiology image processing in Bethesda, Maryland, USA. Specific interest areas are deep learning, image segmentation, modeling, visualization, pattern recognition, computer-aided diagnosis, multi-organ models, atlases and registration. In particular, advanced skills in image processing (computer vision, mathematical modeling, optimization, machine learning) are sought. The researcher will work closely with staff scientists, imaging specialists and clinicians and have access to state-of-the-art whole body MRI, MRI-PET, low-dose CT scanners, advanced graphics workstations and parallel processing/GPU clusters.



Basic Qualifications: Ph.D. in Computer Science, Electrical Engineering, or related discipline with experience in Computer Vision, Machine Learning, or Image Understanding domain, along with successful demonstration of key responsibilities.



Desirable Qualifications: Strong theoretical and practical background in computer vision, image and video analysis, such as object detection and recognition, statistical pattern recognition, machine learning, sparse methods and applied optimization. Prior knowledge about medical imaging is a plus but not a must. Enthusiasm in solving real world clinical imaging problems using large datasets, and hands-on coding skills and ability in C++ and Matlab.



This appointment is for one year and is renewable thereafter on a periodic basis (up to five years). Applications should include a CV, brief statement of research interests and three letters of reference. DHHS and NIH are Equal Opportunity Employers.



Application Instructions:

Email application materials to Dr. Ronald Summers at rms@nih.gov.





--

Daniel C. Elton, Ph.D. | Staff Scientist (contractor)

Imaging Biomarkers and Computer-Aided Diagnosis (CAD) Laboratory
Radiology and Imaging Sciences
National Institutes of Health Clinical Center

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Post Doctoral Fellow Position on Fair AI at University of Arkansas

The Department of Computer Science and Computer Engineering at the University of Arkansas has an opening for a one-year postdoc position. The position will start on January 1, 2020 and could be renewed depending on the performance.



The Post Doctoral Fellow scientist in this position will conduct research projects on fairness aware artificial intelligence, federated learning, dynamic sequence data modeling, and recommendation. The Post Doctoral Fellow scientist will work with the supervisor, Prof. Xintao Wu, to develop prototype systems, publish research papers, write grant proposals, supervise doctoral students' work, and conduct outreach and educational activities on research projects.



Applicants should have 1) a PhD in computer science or related areas such as computer engineering, information technology, or statistics by the start date of employment; 2) strong academic research track record in machine learning, deep learning, big data analytics, or statistics; 3) good programming skills in machine learning and deep learning; and 4) proficiency in both written and spoken English. Experience in fair machine learning, distributed learning, and causal inference are preferred. Experience of writing proposals and/or supervising students is desired.



To be considered, applicants will need to submit a curriculum vitae, cover letter/letter of application that describes personal research interests and why you are interested in this position; unofficial/official trannoscript(s) from graduate education; and a list of three professional references (name, noscript, email address and contact number). The online application from is at https://jobs.uark.edu/postings/38067



The selection process will begin December 15, 2019, and continue until the position is filled. Inquiries or questions may be directed to Dr. Xintao Wu at xintaowu@uark.edu.



About the University



Founded in 1871, the University of Arkansas is a land grant institution, classified by the Carnegie Foundation among the nation's top 2 percent of universities with the highest level of research activity. The University of Arkansas works to advance Arkansas and build a better world through education, research and outreach by providing transformational opportunities and skills, promoting an inclusive and diverse culture and climate, and nurturing creativity, discovery and the spread of new ideas and innovations. Ten colleges and schools serve more than 27,600 students with 200+ academic programs. U of A students earn nationally competitive awards at an impressive rate and represent all 50 states and 120 countries.

The University of Arkansas campus is located in Fayetteville, a welcoming community ranked as one of the best places to live in the U.S. The growing region surrounding Fayetteville is home to numerous Fortune 500 companies and one of the nation's strongest economies. Northwest Arkansas is also quickly gaining a national reputation for its focus on the arts and overall quality of life. Arkansas is a natural wonder of forests, mountains and lakes framed by picturesque rivers and streams. Some of the best outdoor amenities and most spectacular hiking trails are a short drive from campus.

As an employer, the University of Arkansas offers a vibrant work environment and a workplace culture that promotes a healthy work-life balance. The benefits package includes university contributions to health, dental, life and disability insurance, tuition waivers for employees and their families, 12 official holidays, immediate leave accrual, and a choice of retirement programs with university contributions ranging from 5 to 10% of employee salary.

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Research Fellow / Senior Research Fellow in Machine Learning for Climate Science at UCL

We are looking for a Research Fellow / Senior Research Fellow at the intersection of climate science and machine learning.

Climate change is one of the key challenges of the 21st century, and climate action is one of the UN’s sustainable development goals. The goal of this project is to work with climate and environmental data and work with collaborators (e.g., the UK Met Office) toward better statistical climate modeling and data-driven forecasting. This project lies at the intersection of climate/environmental science and statistical machine learning.

You will be part of the Statistical Machine Learning Group, which is located at UCL's Centre for Artificial Intelligence. We have a strong background in probabilistic modeling, data-efficient machine learning, and reinforcement learning. The successful applicant will lead and contribute to research projects at the intersection of machine learning and climate science. They are also expected to contribute to student supervision and interact with research and project partners. The key objective is to investigate the usefulness of statistical machine learning models (e.g., spatio-temporal models, deep probabilistic models) to support climate science and climate action, e.g., by designing faster simulators and predictors or developing specific tools for analyzing environmental data.

The post is graded as Grade 7 or Grade 8, with starting salary in the range £35,965 to £43,470 (Grade 7) or £44,674 to £52,701 (Grade 8) per annum (including London Allowance). Progression through the salary scale is incremental.

The funding for this post is for 24 months in the first instance.

More details and a link to the application can be found at
https://atsv7.wcn.co.uk/search_engine/jobs.cgi?SID=amNvZGU9MTg0MDc3OCZ2dF90ZW1wbGF0ZT05NjYmb3duZXI9NTA0MTE3OCZvd25lcnR5cGU9ZmFpciZicmFuZF9pZD0wJnZhY194dHJhNTA0MTE3OC41MF81MDQxMTc4PTkyNzg2JnZhY3R5cGU9MTI3NiZwb3N0aW5nX2NvZGU9MjI0

Please contact me if you have questions: m.deisenroth@ucl.ac.uk

I will also be at NeurIPS this month, and I'm happy to discuss opportunities in person.

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Machine learning / statistics postdoc at Microsoft Research New England

Qualifications
PhD in computer science, statistics, electrical engineering, mathematics, or a related field
Research ability demonstrated by journal and conference publications
Research agenda that overlaps with ML and statistics group
Participation and activity in the scientific community
Strong communication skills
The ability to work in a highly collaborative and interdisciplinary environment
This role is not to exceed two years

More info

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Postdoc position in Immersive Analytics in France (6 months from Jan/March 2020)

Denoscription
The postdoctoral researcher will extend the work we have been carrying on how to visualize and interact with 3D scatterplots in Virtual Reality (see references below). His/her first task is to develop an experiment that aims to compare two types of data immersion. Other tasks and research directions will depend on his/her personal interest.

Dates
Around 6 months, beginning from January to March, ending no later than October 2020.

Environment
The lab is located in the vibrant city of Nantes (https://en.nantes.fr/home.html), close to the beautiful Atlantic coast of South Brittany. Nantes has been named the 2019 European Capital of Innovation.

Benefits
Net monthly salary > 2000€, depending on experience.

Supervision
Pr. Yannick Prié

Candidate:
PhD in Data Visualization or HCI with a strong interest for immersive technologies and experience in formal evaluation. Good written and oral communication skills.

Contact
Send CV + motivation + references to yannick.prie@univ-nantes.fr

Applications will be considered until the position is filled.

References (see http://yannickprie.net/publications/)
- Survey of Immersive Analytics (2019) Adrien Fonnet, Yannick Prié. in IEEE Transactions on Visualization and Computer Graphics, 22 pp.
- Prototyping Immersive Analytics: Experiments with Design Students (2019) Adrien Fonnet, Grégoire Cliquet, Yannick Prié. in Workshop on Immersive Analytics “Interaction Design and Prototyping for Immersive Analytics” at CHI 2019, Glasgow, UK, May 2019.
- Axes and Coordinate Systems Representations for Immersive Analytics of Multi-Dimensional Data (2018) Adrien Fonnet, Toinon Vigier, Grégoire Cliquet, Fabien Picarougne, Yannick Prié. in 4th International Symposium on Big Data Visual and Immersive Analytics BDVA 2018, Konstanz, Germany, October 2018.
- Immersive Data Exploration and Analysis (2018) Adrien Fonnet, Florian Melki, Yannick Prié, Fabien Picarougne, Grégoire Cliquet. in Student Interaction Design Research conference, May 2018, Helsinki, Finland.
- Towards HMD-based Immersive Analytics (2017) Grégoire Cliquet, Matthieu Perreira, Fabien Picarougne, Yannick Prié, Toinon Vigier. in Immersive analytics Workshop, IEEE VIS 2017, Phoenix, Arizona, United States.

More info

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AUSTRALIA AWARDS SCHOLARSHIPS
Australia Awards Scholarships (AAS) aim to contribute to the long term development needs of Australia's partner countries in line with bilateral and regional agreements. They provide opportunities for people from developing countries to undertake full time undergraduate or postgraduate study at participating Australian universities and Technical and Further Education (TAFE) institutions. The study and research opportunities provided by Australia Awards Scholarships develop skills and knowledge of individuals to drive change and contribute to the development outcomes of their own country.

More info

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PostDoc position on "5D Light Field Video Analysis"

In addition to my earlier announcement it's a pleasure to announce that we now also have the opportunity
to hire a POSTDOC for the analysis and processing of so called 5D Light Field Video.
With the 5th domain we denote flexibly controlling the exposure times of cameras within a light field rig.
Such control enables sub-framing and hence the exchange of temporal with spatial resolution. Only few
tools are available to handle such sub-framed light field videos.

The focus of the position is on optimizing the sub-frame structure by scene analysis and on
spatio/angular/temporal light field interpolation. We use classical as well as deep learning-based
methods and have built an own camera rig with 64 FullHD cameras and very flexible shutter control.

Saarland Informatics Campus offers a very active and recognized international research environment
for visual computing and hence is looking forward to applications by PostDocs with experience on
multi-view and/or light field video processing.

Positions come with a full-time employment at Saarland University for a duration of up to three years
(dependent on the starting date and the qualification of the applicants). Salary follows the German
University employment rules (TV-L13, ~45.000€ p.a. gross salary, exact amounts dependent on the
personal status of the applicant).

Interested researchers are invited to send a mail to Thorsten Herfet and discuss in a more detailed
fashion.

Thorsten Herfet





Prof. Dr.-Ing. Thorsten Herfet
Telecommunications Lab
Saarland Informatics Campus C6 3 10.02


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