Hi everyone,
We need talented person for MCI [Hamrah E Avval] Infrastructure Team that familiar with Cisco devices in Campus LAN and Wireless.
Please send your resume to my email address: fjasemi@gmail.com
We need talented person for MCI [Hamrah E Avval] Infrastructure Team that familiar with Cisco devices in Campus LAN and Wireless.
Please send your resume to my email address: fjasemi@gmail.com
اطلاع رسانی دانشجویان pinned «یک شرکت تحقیقاتی معتبر در حوزه مهندسی پزشکی، در راستای گسترش و تکمیل کادر برنامه نویسی خود، اقدام به جذب افراد مستعد با شرایط کاری مناسب می نماید. همکاری به صورت تمام وقت میسر است. نیازمندی ها: -روحیه یادگیری و پیشرفت -توانایی کار تیمی -تعهد و انگیزه بالا…»
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سلام دوستان، این پیام را یکی از دوستانم فرستاده، اگر کسی با این شرایط می شناسید تا بهش معرفی کنیم.
Hi. I can find a PhD student.
If you know strong master students who interested to do his PhD in England plz share his resume and potential proposals to interview.
Also the proposal needs to related to 5G if possible.
University of surrey, Guildford, England
Hi. I can find a PhD student.
If you know strong master students who interested to do his PhD in England plz share his resume and potential proposals to interview.
Also the proposal needs to related to 5G if possible.
University of surrey, Guildford, England
Forwarded from Apply Now
PhD Student Position for the topic “self-organized collective patterns on graphs
#دکترا
#آلمان
Jacobs University is a private, state-accredited, English-language research university in Bremen. We are offering PreDegree, Bachelor, Master or PhD programs in three focus areas: Health, Mobility and Diversity and are involved in the professional development of specialists and managers and in the transfer of knowledge. Our principles are first class research and teaching, international diversity and transdisciplinary cooperation. As an international university we attract highly talented and open-minded students from all over the world. Currently, more than 1,400 students from 110 nations live and study on our residential campus.
The department „ Life Sciences & Chemistry” invites interested candidates to apply for the next possible date for a
PhD Student Position for the topic “self-organized collective patterns on graphs” (m/f/d)
(Full-time, Start February 2020, limited for 3 years)
Job ID 19 -78
PhD Research project
The relationship between network architecture and dynamics is often assessed on a link-by-link level, e.g., evaluating pairwise correlations among nodes and comparing them with the set of links in a network.
Over the last few years, however, diverse studies have established that many forms of dynamics self-organize on networks to give rise to large-scale collective patterns. The dynamical pattern is then a consequence of the parameters defining the dynamics as well as the network architecture. Examples include Turing patterns on graphs arising from reaction-diffusion systems, self-organized waves around hubs arising in excitable dynamics and synchronization of modules arising in coupled oscillators. These self-organized, collective behaviours are in the focus of this computational project.
This project will employ numerical simulations of simple dynamical processes on graphs to better understand and classify such collective patterns in networks. Based on these theoretical investigations, the relevance of such patterns to the diverse application domains in the ITN will be assessed and the findings will be validated using the rich data resources of the ITN.
Context:
This research fellowship programme (PhD) will be carried out within the context of the i-CONN network, a Marie Skłodowska-Curie Actions– Innovative Training Network (ITN) – project funded by the European Commission, under their H2020 program. Through the project activities, the Fellows/PhD students will have the opportunity to come in contact and collaborate with some of the best European research groups. English is the official language of the i-CONN project. Additional details are available in “Further particulars”.
Your research responsibilities:
Perform high quality research in the bespoke research project under the guidance of the supervisory team.
Meet the members of the supervisory team on a regular basis.
Participate in the activities of the Network as specified in the Grant Agreement and/or required by the node coordinator, including secondments in other network nodes and taking part in the network meetings and in the training activities.
Write up the results of the research activity and present research papers and publications at meetings and conferences, as advised by the supervisors, and contribute to the overall goals of the network.
Widen the personal knowledge in the research area and undertake complementary training.
Keep records of the activities, such as research, training, secondments, visits, leave of absence, etc.
Your qualifications:
The successful candidates must satisfy the eligibility criteria (see below) and have:
An excellent academic record in a quantitative discipline, including, but not restricted to: Computational Systems Biology, Bioinformatics, Statistical Physics, Data Science, Applied Mathematics, Computer Science or related areas.
A keen interest in pursuing research in the development of Connectivity Science.
The ability to work independently and as a member of a rese
#دکترا
#آلمان
Jacobs University is a private, state-accredited, English-language research university in Bremen. We are offering PreDegree, Bachelor, Master or PhD programs in three focus areas: Health, Mobility and Diversity and are involved in the professional development of specialists and managers and in the transfer of knowledge. Our principles are first class research and teaching, international diversity and transdisciplinary cooperation. As an international university we attract highly talented and open-minded students from all over the world. Currently, more than 1,400 students from 110 nations live and study on our residential campus.
The department „ Life Sciences & Chemistry” invites interested candidates to apply for the next possible date for a
PhD Student Position for the topic “self-organized collective patterns on graphs” (m/f/d)
(Full-time, Start February 2020, limited for 3 years)
Job ID 19 -78
PhD Research project
The relationship between network architecture and dynamics is often assessed on a link-by-link level, e.g., evaluating pairwise correlations among nodes and comparing them with the set of links in a network.
Over the last few years, however, diverse studies have established that many forms of dynamics self-organize on networks to give rise to large-scale collective patterns. The dynamical pattern is then a consequence of the parameters defining the dynamics as well as the network architecture. Examples include Turing patterns on graphs arising from reaction-diffusion systems, self-organized waves around hubs arising in excitable dynamics and synchronization of modules arising in coupled oscillators. These self-organized, collective behaviours are in the focus of this computational project.
This project will employ numerical simulations of simple dynamical processes on graphs to better understand and classify such collective patterns in networks. Based on these theoretical investigations, the relevance of such patterns to the diverse application domains in the ITN will be assessed and the findings will be validated using the rich data resources of the ITN.
Context:
This research fellowship programme (PhD) will be carried out within the context of the i-CONN network, a Marie Skłodowska-Curie Actions– Innovative Training Network (ITN) – project funded by the European Commission, under their H2020 program. Through the project activities, the Fellows/PhD students will have the opportunity to come in contact and collaborate with some of the best European research groups. English is the official language of the i-CONN project. Additional details are available in “Further particulars”.
Your research responsibilities:
Perform high quality research in the bespoke research project under the guidance of the supervisory team.
Meet the members of the supervisory team on a regular basis.
Participate in the activities of the Network as specified in the Grant Agreement and/or required by the node coordinator, including secondments in other network nodes and taking part in the network meetings and in the training activities.
Write up the results of the research activity and present research papers and publications at meetings and conferences, as advised by the supervisors, and contribute to the overall goals of the network.
Widen the personal knowledge in the research area and undertake complementary training.
Keep records of the activities, such as research, training, secondments, visits, leave of absence, etc.
Your qualifications:
The successful candidates must satisfy the eligibility criteria (see below) and have:
An excellent academic record in a quantitative discipline, including, but not restricted to: Computational Systems Biology, Bioinformatics, Statistical Physics, Data Science, Applied Mathematics, Computer Science or related areas.
A keen interest in pursuing research in the development of Connectivity Science.
The ability to work independently and as a member of a rese
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Master Internship in Recognition of Sport Gestures with Hierarchical Deep NNs Research Internship proposal
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
Forwarded from Apply Now
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.
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
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.
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
Telegram
Apply Now
https://news.1rj.ru/str/Apply_Now_Andishehsazan
📑اعلام پوزیشنهای مستر و دکتری و پستداک با فاند
📑اعلام پوزیشنهای مستر و دکتری و پستداک با فاند
Forwarded from Apply Now
Master R2 Internship in Natural Language Processing: weakly supervised learning for hate speech detection
Motivations and context
Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.
Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).
More info
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
Motivations and context
Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.
Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).
More info
🌐
https://news.1rj.ru/str/Apply_Now_Andishehsazan
Telegram
Apply Now
https://news.1rj.ru/str/Apply_Now_Andishehsazan
📑اعلام پوزیشنهای مستر و دکتری و پستداک با فاند
📑اعلام پوزیشنهای مستر و دکتری و پستداک با فاند
چهارمین دوره ی جشنواره بازی های رایانه ای،فکری و محیطی – لیگا
http://ligaofficial.ir/%da%86%d9%87%d8%a7%d8%b1%d9%85%db%8c%d9%86-%d8%af%d9%88%d8%b1%d9%87-%db%8c-%d8%ac%d8%b4%d9%86%d9%88%d8%a7%d8%b1%d9%87-%d8%a8%d8%a7%d8%b2%db%8c-%d9%87%d8%a7%db%8c-%d8%b1%d8%a7%db%8c%d8%a7%d9%86%d9%87/
http://ligaofficial.ir/%da%86%d9%87%d8%a7%d8%b1%d9%85%db%8c%d9%86-%d8%af%d9%88%d8%b1%d9%87-%db%8c-%d8%ac%d8%b4%d9%86%d9%88%d8%a7%d8%b1%d9%87-%d8%a8%d8%a7%d8%b2%db%8c-%d9%87%d8%a7%db%8c-%d8%b1%d8%a7%db%8c%d8%a7%d9%86%d9%87/
Training Sessions - Shiraz.pdf
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Training Sessions - Shiraz.pdf
♦استخدام ارتش در استان فارس
🔹مسئول استخدام ارتش در جنوب کشور: در حال حاضر دانشگاه افسری امام علی(ع) در نیروی زمینی و مقطع دیپلم تجربی و ریاضی که معدل کل آنها بالای 14 و معدل کتبی آنان 10 باشد،اقدام به پذیرش نیرو میکند.
🔹دانشآموزانی که در پایه 12 مشغول به تحصیل هستند میتوانند برای پذیرش و استخدام در ارتش اقدام کنند.
🔹برای مقاطع بالاتر از دیپلم نیز در شهریورماه هر سال ارتش جمهوری اسلامی امکان استخدام فراهم میکند.
🔹 متقاضیان تا ۱۵ دی ماه امسال فرصت ثبت نام دارند.
🔹مسئول استخدام ارتش در جنوب کشور: در حال حاضر دانشگاه افسری امام علی(ع) در نیروی زمینی و مقطع دیپلم تجربی و ریاضی که معدل کل آنها بالای 14 و معدل کتبی آنان 10 باشد،اقدام به پذیرش نیرو میکند.
🔹دانشآموزانی که در پایه 12 مشغول به تحصیل هستند میتوانند برای پذیرش و استخدام در ارتش اقدام کنند.
🔹برای مقاطع بالاتر از دیپلم نیز در شهریورماه هر سال ارتش جمهوری اسلامی امکان استخدام فراهم میکند.
🔹 متقاضیان تا ۱۵ دی ماه امسال فرصت ثبت نام دارند.