Complex Systems and Data Science methods to study behavioural patterns in audiovisual contents consumption
https://euraxess.ec.europa.eu/jobs/499679
https://euraxess.ec.europa.eu/jobs/499679
EURAXESS
Complex Systems and Data Science methods to study behavioural patterns in audiovisual contents consumption
We offer an exciting position at the Universitat de Barcelona for a 30 months multidisciplinary project that involves a larger number of partners (companies and universities) in Catalonia. She/heIt is expected to develop dynamical and non-dynamical data driven…
Introduction to Computation & Data Sciecne.
The hands-on Python implementation as soon as a topic is explained explicitly is the thing that attracted me. I found this course to be so intuitive as well.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/lecture-videos/
The hands-on Python implementation as soon as a topic is explained explicitly is the thing that attracted me. I found this course to be so intuitive as well.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/lecture-videos/
MIT OpenCourseWare
Lecture Videos | Introduction to Computational Thinking and Data Science | Electrical Engineering and Computer Science | MIT OpenCourseWare
MIT OpenCourseWare is a web based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity
Applications Open for “Advancing Cancer Biology at the Frontiers of Machine Learning” Innovation Lab
https://insidehpc.com/2020/03/applications-open-for-advancing-cancer-biology-at-the-frontiers-of-machine-learning-innovation-lab/
https://insidehpc.com/2020/03/applications-open-for-advancing-cancer-biology-at-the-frontiers-of-machine-learning-innovation-lab/
insideHPC
Applications Open for “Advancing Cancer Biology at the Frontiers of Machine Learning” Innovation Lab
The National Cancer Institute (NCI) in collaboration with Carnegie Mellon University, and Knowinnovation are convening experts in cancer systems biology, [...]
Official information of Coronavirus disease 2019 (COVID-19) in South Korea is available in Kaggle
https://www.kaggle.com/kimjihoo/coronavirusdataset
https://www.kaggle.com/kimjihoo/coronavirusdataset
Kaggle
[NeurIPS 2020] Data Science for COVID-19 (DS4C)
DS4C: Data Science for COVID-19 in South Korea
Postdoc position in ML/stats at the University of Chicago
Applications are invited for a postdoctoral researcher working under the supervision of Prof. Bryon Aragam in statistics, machine learning, and/or optimization within the Econometrics and Statistics group at the Booth School of Business of the University of Chicago. Potential candidates should have a background in statistics and machine learning, for example nonconvex optimization, nonparametric statistics, and/or learning theory. Applications of interest include causal inference, representation learning, personalization, and graphical models, but may also depend on the candidate’s individual research interests. There will be an emphasis on theoretical/mathematical problems as well as computational/applied work, with a particular focus on problems at the intersection.
The Econometrics and Statistics group at the University of Chicago is diverse and rapidly growing, with 12 full-time faculty working in diverse areas such as statistical machine learning, causal inference, Bayesian statistics, financial econometrics, and forecasting.
Qualifications
The candidate should have a recent Ph.D. degree (or all-but-dissertation) in statistics, computer science, mathematics, or a related area, and should be proficient in programming in Python or R. We welcome applications from candidates with diverse and/or nontraditional backgrounds.
To Apply
Interested candidates should email bryon@chicagobooth.edu to indicate their interest in this position.
Required Documents
1) Resume/CV
2) Cover letter, including brief description of research interests
3) Graduate transcripts
4) At least one academic reference
Application link: https://uchicago.wd5.myworkdayjobs.com/en-US/External/job/Hyde-Park-Campus/Principal-Researcher_JR07406
Applications are invited for a postdoctoral researcher working under the supervision of Prof. Bryon Aragam in statistics, machine learning, and/or optimization within the Econometrics and Statistics group at the Booth School of Business of the University of Chicago. Potential candidates should have a background in statistics and machine learning, for example nonconvex optimization, nonparametric statistics, and/or learning theory. Applications of interest include causal inference, representation learning, personalization, and graphical models, but may also depend on the candidate’s individual research interests. There will be an emphasis on theoretical/mathematical problems as well as computational/applied work, with a particular focus on problems at the intersection.
The Econometrics and Statistics group at the University of Chicago is diverse and rapidly growing, with 12 full-time faculty working in diverse areas such as statistical machine learning, causal inference, Bayesian statistics, financial econometrics, and forecasting.
Qualifications
The candidate should have a recent Ph.D. degree (or all-but-dissertation) in statistics, computer science, mathematics, or a related area, and should be proficient in programming in Python or R. We welcome applications from candidates with diverse and/or nontraditional backgrounds.
To Apply
Interested candidates should email bryon@chicagobooth.edu to indicate their interest in this position.
Required Documents
1) Resume/CV
2) Cover letter, including brief description of research interests
3) Graduate transcripts
4) At least one academic reference
Application link: https://uchicago.wd5.myworkdayjobs.com/en-US/External/job/Hyde-Park-Campus/Principal-Researcher_JR07406
Reduce model size to train/test faster.
However, you should actually increase model size to speed up training and inference for transformers
Speeding Up Transformer Training and Inference By Increasing Model Size
https://bair.berkeley.edu/blog/2020/03/05/compress/
paper https://arxiv.org/pdf/2002.11794.pdf
However, you should actually increase model size to speed up training and inference for transformers
Speeding Up Transformer Training and Inference By Increasing Model Size
https://bair.berkeley.edu/blog/2020/03/05/compress/
paper https://arxiv.org/pdf/2002.11794.pdf
The Berkeley Artificial Intelligence Research Blog
Speeding Up Transformer Training and Inference By <i>Increasing</i> Model Size
The BAIR Blog
How a research scientist built Kornia: an open source differentiable library for PyTorch
https://medium.com/pytorch/how-a-research-scientist-built-kornia-an-open-source-differentiable-library-for-pytorch-16e2aa758bc8
https://medium.com/pytorch/how-a-research-scientist-built-kornia-an-open-source-differentiable-library-for-pytorch-16e2aa758bc8
Medium
How a research scientist built Kornia: an open source differentiable library for PyTorch
This is a blog post covering an interview with Edgar Riba, the founding member of Kornia, an open source differentiable CV library.
Diet modulates brain network stability, a biomarker for brain aging, in young adults
https://www.pnas.org/content/early/2020/03/02/1913042117.long
https://www.pnas.org/content/early/2020/03/02/1913042117.long
PNAS
Diet modulates brain network stability, a biomarker for brain aging, in young adults
To better understand how diet influences brain aging, we focus here on the presymptomatic period during which prevention may be most effective. Large-scale life span neuroimaging datasets show functional communication between brain regions destabilizes with…