Forwarded from Apply Time
#Phrase
📋 فهرستی از #واژههایی که در این کانال با آنها تاکنون مواجه شده، یا در ادامه خواهید شد:
📝 1- Scholarship, Fund, Grant, Bursary:
🔎بورسیهی تحصیلی یا همان کمک هزینهی تحصیلی (خواه مبلغی که کل هزینههای شما را پوشش میدهد، و خواه بخشی از آن را).
📝 2- Fully-Funded Scholarship:
🔎 بورسیهی تحصیلیای که کل هزینهی شما را پوشش میدهد.
📝 3- Partially-Funded Scholarship:
🔎 بورسیهی تحصیلیای که بخشی از هزینهها را پوشش میدهد.
📝 4- Tuition fee:
🔎 شهریهی دانشگاه.
که لزوما همهی دانشگاهها شهریه ندارند. برای مثال عموماً دانشگاههای آلمان تقریبا بدون شهریه هستند. در حالیکه اکثریت دانشگاههای انگلستان شهریه دارند.
📝 5- Costs of living:
🔎هزینههای زندگی.
📝 6- Tuition fee waiver award:
🔎 اشاره به بورسیهای تحصیلیای دارد که جهت پرداخت بابت کل یا بخشی از شهریه میباشد. که معمولا مستقیم به حساب دانشگاه واریز میشود نه به حساب دانشجو.
📝 7- Stipend:
🔎 حقوق. مقرری.
📝 8- Position:
🔎 منظور یک موقعیت تحصیلی است. به عنوان مثال:
PhD position
اشاره به یک موقعیت تحصیلی در مقطع دکتری دارد. یا
Faculty position
اشاره به جایگاه هیئت علمی دارد.
📝 9- Open PhD position:
🔎 منظور این است که استاد، آزمایشگاه، دپارتمان (دانشکده)، یا دانشگاه مورد نظر جایگاههایی دارند برای استخدام دانشجویان دکتری.
📝 10- Inquiry:
🔎 پرس و جو کردن.
به عنوان مثال وقتی شما به استادی ایمیل میزنید و از او جویا میشوید که آیا میتواند برای ترم آتی دانشجو بگیرد یا خیر؟؛ شما دارید از او پرس و جو میکنید.
📝 11- Recruit:
🔎 استخدام کردن.
به عنوان مثال، استاد یا دانشگاهی شما را برای مقطع دکتری استخدام میکند.
📝 12- Admission:
🔎 گروه مدیریتی یک دانشکده (یا دانشگاه)، مثلا گروه دانشکده برق: کسانی که مدارک شما را بررسی میکنند تا در صورت مناسب بودن به شما پاسخ مثبت دهند.
📝 13- Application:
🔎 منظور درخواست شما و مجموعه مدارکی است که شما برای استاد، آزمایشگاه، دپارتمان یا ادمیشن ارسال میکنید تا بررسی شوند و به شما پاسخی دهند.
📝 14- Offer:
🔎 پیشنهاد دادن.
وقتی دانشگاهی اپلیکیشن شما را مثبت ارزیابی کرد، به شما آفر یا اصطلاحا ادمیشن میدهد.
📝 15- BA, BS or Undergraduate
🔎دورهی لیسانس
📝 16- MA, MS or Graduate
🔎 دورهی فوقلیسانس
📝 17- PhD or Graduate
🔎 دورهی دکتری
📝 18- Postdoc
🔎 دورهی پسادکتری
📝 19- Funded
🔎 نشان میدهد که پوزیشن مورد نظر همراه با فاند است. خواه فاند کامل خواه ناکامل.
البته تمامی پوزیشنهای معرفی شده در کانال با فاند همراه هستند.
📝 20- Apply Time:
🔎 احتمالا بهترین همراه شما در امر پذیرش و اعزام به خارج از کشور.
✔️ @ApplyTime
📋 فهرستی از #واژههایی که در این کانال با آنها تاکنون مواجه شده، یا در ادامه خواهید شد:
📝 1- Scholarship, Fund, Grant, Bursary:
🔎بورسیهی تحصیلی یا همان کمک هزینهی تحصیلی (خواه مبلغی که کل هزینههای شما را پوشش میدهد، و خواه بخشی از آن را).
📝 2- Fully-Funded Scholarship:
🔎 بورسیهی تحصیلیای که کل هزینهی شما را پوشش میدهد.
📝 3- Partially-Funded Scholarship:
🔎 بورسیهی تحصیلیای که بخشی از هزینهها را پوشش میدهد.
📝 4- Tuition fee:
🔎 شهریهی دانشگاه.
که لزوما همهی دانشگاهها شهریه ندارند. برای مثال عموماً دانشگاههای آلمان تقریبا بدون شهریه هستند. در حالیکه اکثریت دانشگاههای انگلستان شهریه دارند.
📝 5- Costs of living:
🔎هزینههای زندگی.
📝 6- Tuition fee waiver award:
🔎 اشاره به بورسیهای تحصیلیای دارد که جهت پرداخت بابت کل یا بخشی از شهریه میباشد. که معمولا مستقیم به حساب دانشگاه واریز میشود نه به حساب دانشجو.
📝 7- Stipend:
🔎 حقوق. مقرری.
📝 8- Position:
🔎 منظور یک موقعیت تحصیلی است. به عنوان مثال:
PhD position
اشاره به یک موقعیت تحصیلی در مقطع دکتری دارد. یا
Faculty position
اشاره به جایگاه هیئت علمی دارد.
📝 9- Open PhD position:
🔎 منظور این است که استاد، آزمایشگاه، دپارتمان (دانشکده)، یا دانشگاه مورد نظر جایگاههایی دارند برای استخدام دانشجویان دکتری.
📝 10- Inquiry:
🔎 پرس و جو کردن.
به عنوان مثال وقتی شما به استادی ایمیل میزنید و از او جویا میشوید که آیا میتواند برای ترم آتی دانشجو بگیرد یا خیر؟؛ شما دارید از او پرس و جو میکنید.
📝 11- Recruit:
🔎 استخدام کردن.
به عنوان مثال، استاد یا دانشگاهی شما را برای مقطع دکتری استخدام میکند.
📝 12- Admission:
🔎 گروه مدیریتی یک دانشکده (یا دانشگاه)، مثلا گروه دانشکده برق: کسانی که مدارک شما را بررسی میکنند تا در صورت مناسب بودن به شما پاسخ مثبت دهند.
📝 13- Application:
🔎 منظور درخواست شما و مجموعه مدارکی است که شما برای استاد، آزمایشگاه، دپارتمان یا ادمیشن ارسال میکنید تا بررسی شوند و به شما پاسخی دهند.
📝 14- Offer:
🔎 پیشنهاد دادن.
وقتی دانشگاهی اپلیکیشن شما را مثبت ارزیابی کرد، به شما آفر یا اصطلاحا ادمیشن میدهد.
📝 15- BA, BS or Undergraduate
🔎دورهی لیسانس
📝 16- MA, MS or Graduate
🔎 دورهی فوقلیسانس
📝 17- PhD or Graduate
🔎 دورهی دکتری
📝 18- Postdoc
🔎 دورهی پسادکتری
📝 19- Funded
🔎 نشان میدهد که پوزیشن مورد نظر همراه با فاند است. خواه فاند کامل خواه ناکامل.
البته تمامی پوزیشنهای معرفی شده در کانال با فاند همراه هستند.
📝 20- Apply Time:
🔎 احتمالا بهترین همراه شما در امر پذیرش و اعزام به خارج از کشور.
✔️ @ApplyTime
Privacy in Machine Learning and Artificial Intelligence (ICML’18 Workshop)
Submission deadline: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Workshop date: July 13/14/15, 2018, Stockholm
Website: https://pimlai.github.io/pimlai18/
News: We have travel grants available to support students and early career researchers interested in attending the workshop. Application deadline is June 4. See the workshop website for more information.
✔️ @ApplyTime
Submission deadline: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Workshop date: July 13/14/15, 2018, Stockholm
Website: https://pimlai.github.io/pimlai18/
News: We have travel grants available to support students and early career researchers interested in attending the workshop. Application deadline is June 4. See the workshop website for more information.
✔️ @ApplyTime
Postdoc: with Amos Storkey, School of Informatics, University of Edinburgh.
Deadline: 18-May-2018
Title: Deep Learning Methods for Constrained Environments
Requirements: Machine Learning or related PhD or equivalent.
Further details and to apply:
https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=043488
Informal discussion is very welcome: please email amos+postdoc0518@inf.ed.REMOVE.ac.uk with a CV.
✔️ @ApplyTime
Deadline: 18-May-2018
Title: Deep Learning Methods for Constrained Environments
Requirements: Machine Learning or related PhD or equivalent.
Further details and to apply:
https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=043488
Informal discussion is very welcome: please email amos+postdoc0518@inf.ed.REMOVE.ac.uk with a CV.
✔️ @ApplyTime
CFP (June 30): IEEE TNNLS Special Issue on "Recent Advances in Theory, Methodology and Applications of Imbalanced Learning"
Extended Submission Deadline: June 30, 2018.
Learning from imbalanced/unbalanced data (aka imbalanced learning or class-imbalance learning) is a challenging task faced by practitioners from a wide variety of communities. In the last two decades, researchers from various disciplines including data mining, machine learning, pattern recognition and statistics have intensively investigated this theme. However, as pointed out in the 2013 book “Imbalanced Learning: Foundations, Algorithms, and Applications” collectively authored by experts in this field, many if not the most approaches to imbalanced learning are very heuristic and ad hoc, and thus many open questions remain there: “What is the assurance that algorithms specifically designed for imbalanced learning could really help, and how and why?”; “Is there a way we could develop a theoretical guidance on which based learning algorithm is most appropriate for a particular type of imbalanced data?”; “What is the relationship between data-imbalanced ratio and learning model complexity?”, for example. Moreover, in recent years the datasets that practitioners are concerned have grown increasingly rapidly and complexly; many new applications, and thus new types of data and new learning paradigms, have emerged. Therefore, this special issue aims to call for the state-of-the-art research work in the theory, methodology and applications of imbalanced learning, and aims to demonstrate the recent efforts made by the relevant researchers from a wide range of disciplines.
We welcome all the original work on topics regarding new theory, methodology and applications of imbalanced learning, including but not limited to:
* Deep learning for large-scale imbalanced data
* Representation learning for imbalanced data
* Reinforcement learning for imbalanced data
* Active learning and passive learning for imbalanced data
* Transfer learning and concept drift for imbalanced data
* Imbalanced learning in non-stationary environments
* Online learning and incremental learning for imbalanced data
* Statistical modelling for (non-Gaussian) imbalanced data
* Statistical machine learning for imbalanced data
* Discriminative learning and generative learning for imbalanced data
* Similarity/metric learning for imbalanced data
* Ensemble learning for imbalanced data
* Related learning problems: one-class classification, novelty/outlier/anomaly detection
* Theoretical analysis of models and algorithms for imbalanced learning
* New evaluation metrics for imbalanced learning
* New applications of imbalanced learning: 1) Object detection, classification, recognition; 2) Image retrieval, segmentation, understanding; 3) Speech recognition, synthesis, anti-spoofing; 4) Document retrieval, categorization, topic model; 5) Biomedical signal processing, medical image analysis, bioinformatics; 6) Fault detection/diagnosis, fraud detection, cyber-security; and 7) Other related novel applications
IMPORTANT DATES
30 June 2018 -- Deadline for manunoscript submission
31 August 2018 -- Notification of authors
31 October 2018 -- Deadline for submission of revised manunoscripts
31 December 2018 -- Final decision of acceptance
February 2019 -- Tentative publication date
SUBMISSION INSTRUCTIONS
1. Read the information for Authors at http://cis.ieee.org/tnnls.
2. Submit your manunoscript at the TNNLS webpage (http://mc.manunoscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manunoscript and in the cover letter that the manunoscript is submitted to this special issue. Send an email to the leading editor Dr. Jing-Hao Xue (jinghao.xue@ucl.ac.uk) with subject “TNNLS special issue submission” to notify about your submission.
3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.
✔ @ApplyTime
Extended Submission Deadline: June 30, 2018.
Learning from imbalanced/unbalanced data (aka imbalanced learning or class-imbalance learning) is a challenging task faced by practitioners from a wide variety of communities. In the last two decades, researchers from various disciplines including data mining, machine learning, pattern recognition and statistics have intensively investigated this theme. However, as pointed out in the 2013 book “Imbalanced Learning: Foundations, Algorithms, and Applications” collectively authored by experts in this field, many if not the most approaches to imbalanced learning are very heuristic and ad hoc, and thus many open questions remain there: “What is the assurance that algorithms specifically designed for imbalanced learning could really help, and how and why?”; “Is there a way we could develop a theoretical guidance on which based learning algorithm is most appropriate for a particular type of imbalanced data?”; “What is the relationship between data-imbalanced ratio and learning model complexity?”, for example. Moreover, in recent years the datasets that practitioners are concerned have grown increasingly rapidly and complexly; many new applications, and thus new types of data and new learning paradigms, have emerged. Therefore, this special issue aims to call for the state-of-the-art research work in the theory, methodology and applications of imbalanced learning, and aims to demonstrate the recent efforts made by the relevant researchers from a wide range of disciplines.
We welcome all the original work on topics regarding new theory, methodology and applications of imbalanced learning, including but not limited to:
* Deep learning for large-scale imbalanced data
* Representation learning for imbalanced data
* Reinforcement learning for imbalanced data
* Active learning and passive learning for imbalanced data
* Transfer learning and concept drift for imbalanced data
* Imbalanced learning in non-stationary environments
* Online learning and incremental learning for imbalanced data
* Statistical modelling for (non-Gaussian) imbalanced data
* Statistical machine learning for imbalanced data
* Discriminative learning and generative learning for imbalanced data
* Similarity/metric learning for imbalanced data
* Ensemble learning for imbalanced data
* Related learning problems: one-class classification, novelty/outlier/anomaly detection
* Theoretical analysis of models and algorithms for imbalanced learning
* New evaluation metrics for imbalanced learning
* New applications of imbalanced learning: 1) Object detection, classification, recognition; 2) Image retrieval, segmentation, understanding; 3) Speech recognition, synthesis, anti-spoofing; 4) Document retrieval, categorization, topic model; 5) Biomedical signal processing, medical image analysis, bioinformatics; 6) Fault detection/diagnosis, fraud detection, cyber-security; and 7) Other related novel applications
IMPORTANT DATES
30 June 2018 -- Deadline for manunoscript submission
31 August 2018 -- Notification of authors
31 October 2018 -- Deadline for submission of revised manunoscripts
31 December 2018 -- Final decision of acceptance
February 2019 -- Tentative publication date
SUBMISSION INSTRUCTIONS
1. Read the information for Authors at http://cis.ieee.org/tnnls.
2. Submit your manunoscript at the TNNLS webpage (http://mc.manunoscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manunoscript and in the cover letter that the manunoscript is submitted to this special issue. Send an email to the leading editor Dr. Jing-Hao Xue (jinghao.xue@ucl.ac.uk) with subject “TNNLS special issue submission” to notify about your submission.
3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.
✔ @ApplyTime
cis.ieee.org
Information for Authors - IEEE Computational Intelligence Society
From its institution as the Neural Networks Council in the early 1990s, the IEEE Computational Intelligence Society has rapidly grown into a robust community with a vision for addressing real-world issues with biologically-motivated computational paradigms.…
Forwarded from Apply Time
#Course #TOEFL #IELTS #TESOL #Essay #Conversation #English #Writing #Listening #Grammar #ApplyTime
📖 معرفی چند کورس آنلاین زبان انگلیسی، که احتمالا برای شما مفید باشد. (ویدئوهای هر کلاس، قابل دانلود بوده، و همهی کورسها رایگان هستند): 👈
🌐 http://applytime.ir/learn-english-online-free-different-levels-different-skills-different-certifications/
❓کدام یک از موارد فوق را انتخاب کنم؟
💡تفاوتی ندارد. بهتر است بجای زمانی که روی تردیدتان برای انتخاب یکی از این کورسها میگذارید، هر چه سریعتر یکی از آنها را شروع کنید.
✳️ همچنین شما میتوانید در کلاسهای آنلاین و یا حضوری یادگیری زبان اپلایتایم (انگلیسی، فرانسوی، آلمانی) نیز شرکت کنید. در صورت تمایل با ما در ارتباط باشید.
🌈 پل اولیهی ارتباط با ما:
🎭 Admin: @Dr_Apply
✔️ https://news.1rj.ru/str/ApplyTime
📖 معرفی چند کورس آنلاین زبان انگلیسی، که احتمالا برای شما مفید باشد. (ویدئوهای هر کلاس، قابل دانلود بوده، و همهی کورسها رایگان هستند): 👈
🌐 http://applytime.ir/learn-english-online-free-different-levels-different-skills-different-certifications/
❓کدام یک از موارد فوق را انتخاب کنم؟
💡تفاوتی ندارد. بهتر است بجای زمانی که روی تردیدتان برای انتخاب یکی از این کورسها میگذارید، هر چه سریعتر یکی از آنها را شروع کنید.
✳️ همچنین شما میتوانید در کلاسهای آنلاین و یا حضوری یادگیری زبان اپلایتایم (انگلیسی، فرانسوی، آلمانی) نیز شرکت کنید. در صورت تمایل با ما در ارتباط باشید.
🌈 پل اولیهی ارتباط با ما:
🎭 Admin: @Dr_Apply
✔️ https://news.1rj.ru/str/ApplyTime
Forwarded from Apply Time
#Course #TOEFL #IELTS #TESOL #Essay #Conversation #English #Writing #Listening #Grammar #ApplyTime
⚠️ از بین کورسهای زبان انگلیسی معرفی شده در پُست فوق، کورسهای #تافل و #آیلتس توسط تیم اپلایتایم پیشتر دانلود شده و از طریق همین کانال در اختیار شما قرار گرفت.
🖇 شما میتوانید از طریق لینکهای زیر، اطلاعات لازم در مورد آن دو کورس را بدست آورده و آنها را دانلود کنید:
⏺ #TOEFL:
Info: https://news.1rj.ru/str/ApplyTime/959
File: https://news.1rj.ru/str/ApplyTime/960
⏹ #IELTS:
Info: https://news.1rj.ru/str/ApplyTime/1026
Files:
https://news.1rj.ru/str/ApplyTime/1027
https://news.1rj.ru/str/ApplyTime/1028
https://news.1rj.ru/str/ApplyTime/1029
https://news.1rj.ru/str/ApplyTime/1030
https://news.1rj.ru/str/ApplyTime/1031
https://news.1rj.ru/str/ApplyTime/1032
✔️ https://news.1rj.ru/str/ApplyTime
It's learning time. Let's go!
⚠️ از بین کورسهای زبان انگلیسی معرفی شده در پُست فوق، کورسهای #تافل و #آیلتس توسط تیم اپلایتایم پیشتر دانلود شده و از طریق همین کانال در اختیار شما قرار گرفت.
🖇 شما میتوانید از طریق لینکهای زیر، اطلاعات لازم در مورد آن دو کورس را بدست آورده و آنها را دانلود کنید:
⏺ #TOEFL:
Info: https://news.1rj.ru/str/ApplyTime/959
File: https://news.1rj.ru/str/ApplyTime/960
⏹ #IELTS:
Info: https://news.1rj.ru/str/ApplyTime/1026
Files:
https://news.1rj.ru/str/ApplyTime/1027
https://news.1rj.ru/str/ApplyTime/1028
https://news.1rj.ru/str/ApplyTime/1029
https://news.1rj.ru/str/ApplyTime/1030
https://news.1rj.ru/str/ApplyTime/1031
https://news.1rj.ru/str/ApplyTime/1032
✔️ https://news.1rj.ru/str/ApplyTime
It's learning time. Let's go!
http://eaia2018.dcc.fc.up.pt/
Note: EAIA Scholarships for participation available:
http://eaia2018.dcc.fc.up.pt/pdfs/SoBigData.pdf
✔️ @ApplyTime
Note: EAIA Scholarships for participation available:
http://eaia2018.dcc.fc.up.pt/pdfs/SoBigData.pdf
✔️ @ApplyTime
Call for papers
2nd International Workshop on the ApplicatioN of Semantic WEb technologies in Robotics
http://answer.kmi.open.ac.uk/
✔️ @ApplyTime
2nd International Workshop on the ApplicatioN of Semantic WEb technologies in Robotics
http://answer.kmi.open.ac.uk/
✔️ @ApplyTime
2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications
10-14 September, Dublin, Ireland
Website: http://lidta.dcc.fc.up.pt/
✔️ @ApplyTime
10-14 September, Dublin, Ireland
Website: http://lidta.dcc.fc.up.pt/
✔️ @ApplyTime
THE 12TH INTERNATIONAL SYMPOSIUM ON LINEAR DRIVES FOR INDUSTRY APPLICATIONS LDIA2019
Neuchâtel, Switzerland, July 1-3, 2019
https://ldia2019.epfl.ch/
✔️ @ApplyTime
Neuchâtel, Switzerland, July 1-3, 2019
https://ldia2019.epfl.ch/
✔️ @ApplyTime
Dear All,
We are happy to announce that the program for the 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA
2018) is now available at:
http://cvssp.org/events/lva-ica-2018/program/
LVA/ICA 2018 will be the held at the University of Surrey, Guildford, UK from July 2-6, 2018.
Early registration is available until 31 May 2018.
We look forward to welcoming you to Surrey!
Best wishes,
Mark Plumbley
Co-Chair, LVA/ICA 2018
--
Prof Mark D Plumbley
Professor of Signal Processing Centre for Vision, Speech and Signal Processing (CVSSP)
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: m.plumbley@surrey.ac.uk
✔ @ApplyTime
We are happy to announce that the program for the 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA
2018) is now available at:
http://cvssp.org/events/lva-ica-2018/program/
LVA/ICA 2018 will be the held at the University of Surrey, Guildford, UK from July 2-6, 2018.
Early registration is available until 31 May 2018.
We look forward to welcoming you to Surrey!
Best wishes,
Mark Plumbley
Co-Chair, LVA/ICA 2018
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Prof Mark D Plumbley
Professor of Signal Processing Centre for Vision, Speech and Signal Processing (CVSSP)
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: m.plumbley@surrey.ac.uk
✔ @ApplyTime
PhD Position in Machine Learning: Early detection of epidemiological hazards (TU Darmstadt)
https://www.ke.tu-darmstadt.de/staff/jobs/ESEG
✔ @ApplyTime
https://www.ke.tu-darmstadt.de/staff/jobs/ESEG
✔ @ApplyTime
Workshop on Computational Biology (https://sites.google.com/view/wcb2018) in Stockholmsmässan, Stockholm SWEDEN (http://icml.cc/ and https://www.ijcai-18.org/) (July 10-15, 2018).
✔ @ApplyTime
✔ @ApplyTime
Postdoc: multiview learning and neuroimaging, Marseille
Understanding individual differences in neuroimaging using multi-view machine learning. Methods and applications.
We are seeking candidates for a two years postdoctoral, for developping new machine learning methods to deal with heterogeneous data such as anatomical, functional and diffusion MRI. This post-doc will be funded by the newly established Institute for Language, Communication and the Brain in Marseille, France (http://www.ilcb.fr), and will be awarded through a competitive selection process. The laureate will work in both the Institut de Neurosciences de la Timone (http://www.int.univ-amu.fr/) and the Laboratoire d'Informatique et Systèmes (http://www.lis-lab.fr/).
In brain imaging, traditional group analyses rely on averaging data collected in different individuals. This averaging offers a summary representation of the studied group, thus providing a way to perform inference at the population level. However, it discards the specificities of each individual, which have recently proved to carry critical information to develop diagnosis and prognosis tools for neurological and psychiatric diseases or to understand high level cognitive processes.
Estimating robust population-wise invariants while preserving individual specificities is a challenge that can be addressed by integrating the information offered by different neuroimaging modalities, such as anatomical, functional and diffusion MRI, which respectively allow assessing brain shape, activity and connectivity. This can therefore be framed as a multi-view machine learning question. The tasks of the post-doctoral fellow will consist in 1. finding adequate representations of data (e.g. graph, stack of images, …) that preserve structural information, 2. designing and implementing machine learning algorithms that exploit both the representations and the multiple views using kernel methods and/or neural networks, and 3. evaluating them on a variety of MRI datasets dedicated to studying language and communication.
The candidate should have completed a PhD in computer science, applied mathematics or electrical engineering, with a focus on machine learning. He/she should also have a strong motivation to work in neuroscience, as the working environment will be truly inter-disciplinary. Interested candidates should imperatively contact sylvain.takerkart@univ-amu.fr, francois-xavier.dupe@lis-lab.fr and hachem.kadri@lis-lab.fr before May 25 2018 for a first contact.
✔️ @ApplyTime
Understanding individual differences in neuroimaging using multi-view machine learning. Methods and applications.
We are seeking candidates for a two years postdoctoral, for developping new machine learning methods to deal with heterogeneous data such as anatomical, functional and diffusion MRI. This post-doc will be funded by the newly established Institute for Language, Communication and the Brain in Marseille, France (http://www.ilcb.fr), and will be awarded through a competitive selection process. The laureate will work in both the Institut de Neurosciences de la Timone (http://www.int.univ-amu.fr/) and the Laboratoire d'Informatique et Systèmes (http://www.lis-lab.fr/).
In brain imaging, traditional group analyses rely on averaging data collected in different individuals. This averaging offers a summary representation of the studied group, thus providing a way to perform inference at the population level. However, it discards the specificities of each individual, which have recently proved to carry critical information to develop diagnosis and prognosis tools for neurological and psychiatric diseases or to understand high level cognitive processes.
Estimating robust population-wise invariants while preserving individual specificities is a challenge that can be addressed by integrating the information offered by different neuroimaging modalities, such as anatomical, functional and diffusion MRI, which respectively allow assessing brain shape, activity and connectivity. This can therefore be framed as a multi-view machine learning question. The tasks of the post-doctoral fellow will consist in 1. finding adequate representations of data (e.g. graph, stack of images, …) that preserve structural information, 2. designing and implementing machine learning algorithms that exploit both the representations and the multiple views using kernel methods and/or neural networks, and 3. evaluating them on a variety of MRI datasets dedicated to studying language and communication.
The candidate should have completed a PhD in computer science, applied mathematics or electrical engineering, with a focus on machine learning. He/she should also have a strong motivation to work in neuroscience, as the working environment will be truly inter-disciplinary. Interested candidates should imperatively contact sylvain.takerkart@univ-amu.fr, francois-xavier.dupe@lis-lab.fr and hachem.kadri@lis-lab.fr before May 25 2018 for a first contact.
✔️ @ApplyTime
phd_OTDL-@ApplyTime.pdf
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We propose a PhD funding for a student willing to work on optimal transport and deep learning.
Deadline: 4th June 2018
Nicolas Courty
Associate Professor in IRISA
✔️ @ApplyTime
Deadline: 4th June 2018
Nicolas Courty
Associate Professor in IRISA
✔️ @ApplyTime