❇️ https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
💠 dataset: https://pages.semanticscholar.org/coronavirus-research

💠 https://www.matthewtancik.com/nerf

❇️ We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.

❇️ Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
https://github.com/ctgk/PRML

شرکت داده پردازی نیک آفرین جهت تکمیل تیم فنی دپارتمان هوش مصنوعی خود در شهر تهران به دنبال جذب تعدادی نیرو به صورت پاره وقت و تمام وقت در حوزه های تخصصی زیر می باشد:

🔶 بینایی ماشین و پردازش تصویر
🔶 پردازش و تحلیل صوت
🔶 پردازش زبان طبیعی
🔶 پردازش سری های زمانی

❇️ مهارت های اختصاصی
• تسلط به زبان های برنامه نویسی و کتابخانه های رایج یادگیری ماشین/یادگیری عمیق
• آشنایی کافی با زبان انگلیسی برای استفاده از منابع انگلیسی
• تجربه کار کردن با GitHub (مزیت محسوب می شود)
• تجربه کار کردن با سیستم عامل لینوکس و اسکریپت نویسی (مزیت محسوب می شود)
• توانمندی در مستند سازی پروژه (مزیت محسوب می شود)

❇️ ویژگی های فردی
• دارای روحیه تیمی و اخلاق حرفه ای کاری
• با انگیزه و علاقه مند به حل مسئله و ارائه راه حل های جدید
• مسئولیت پذیر، پیگیر و منظم در کارها
• دارای روحیه یادگیری بالا و جستجو در اینترنت

❇️ معرفی شرکت
دپارتمان هوش مصنوعی شرکت داده پردازی نیک آفرین با هدف اولیه پیاده سازی و توسعه سامانه های امنیتی-نظارتی دارای پردازش تصویر مبتنی بر هوش مصنوعی فعالیت خود را آغاز نموده و در حال گسترش فعالیت های خود به دیگر حوزه های صنعتی و تجاری است. پروژه های کنونی این دپارتمان در حوزه های حفاظت و امنیت، حمل و نقل و زیست فناوری می باشد.

☯️ متقاضیان می توانند رزومه کاری خود را به صورت فایل pdf به آدرس ایمیل [email protected] با عنوان "همکاری" ارسال نمایند.

❇️ گروه لینکداین
“Ai & BigData"
https://www.linkedin.com/groups/8721739/

❇️ گروه تلگرام برای پرسش و پاسش و اشتراک گذاری مطاالب
https://t.iss.one/ml_in_science

❇️ Ai / ML / DL Huge Data-Sets Resources ❇️

💠 Google Dataset Search: https://toolbox.google.com/datasetsearch
💠 KD Nuggets: https://www.kdnuggets.com/datasets/index.html
💠 UCI: https://archive.ics.uci.edu/ml/index.php
💠 Kaggle: https://www.kaggle.com/datasets?sortBy=relevance&group=public&search=Retail+&page=1&pageSize=20&size=all&filetype=all&license=all
💠 Facebook: https://bigdataenthusiast.wordpress.com/2016/03/19/mining-facebook-data-using-r-facebook-api/
💠 https://www.analyticsvidhya.com/blog/2016/11/25-websites-to-find-datasets-for-data-science-projects/
💠 https://www.unb.ca/cic/datasets/index.html
💠 https://www.linkedin.com/pulse/ten-sources-free-big-data-internet-alan-brown/?fbclid=IwAR17lpDNxDShh4wiAkHGUC3q9CDc-UPVkQNL2xJNPlcxXcsHxPDj7c402V4
💠 https://ckan.publishing.service.gov.uk/dataset
💠 https://www.springboard.com/blog/free-public-data-sets-data-science-project/
💠 https://bigdata-madesimple.com/70-websites-to-get-large-data-repositories-for-free/
💠 https://www.philippe-fournier-viger.com/spmf/datasets.php
💠 https://www.dunnhumby.com/sourcefiles
💠 https://bigdata-madesimple.com/70-websites-to-get-large-dat…/
💠 https://guides.emich.edu/data/free-data
💠 https://community.tableau.com/docs/DOC-1236
💠 https://fyi.extension.wisc.edu/downtown-market-analysis/understanding-the-market/demographics-and-lifestyle-analysis/
💠 https://www.dunnhumby.com/sourcefiles
💠 https://tech.instacart.com/3-million-instacart-orders-open-sourced-d40d29ead6f2
Computer Science Project ideas for Students:
💠 https://nevonprojects.com/year-projects-for-computer-engineering/

❇️ tslearn is a Python package that provides machine learning tools for the analysis of time series

https://tslearn.readthedocs.io/en/stable/index.html

❇️ CERN 4th IML Machine Learning Workshop
19-22 October 2020
———————————-

Dear ML enthusiasts,

The 4th annual Inter-experiment Machine Learning workshop has been rescheduled to take place at CERN 19th-22 October 2020. It will take place in person, if conditions allow it, but remote participation will also be possible.

Please register to

https://indico.cern.ch/event/852553/

The following 4 days structure is anticipated:

• Monday 19th Oct : hands-on tutorials : 1/2 day hls4ml, 1/2 day Graph Neural Networks by Deepmind
• Tuesday 20th Oct : morning : invited talks (confirmed speakers Peter Battaglia (DeepMind), Ulrich Koethe (U Heidelberg), Amir Farbin (UTA), Kazuhiro Terao (SLAC) , afternoon industry session
• Wednesday 21st/Thursday 22nd : contributed talks



Most importantly, we welcome your contributions! They form the heart of the workshop, and so we strongly encourage you to consider presenting your work.

Abstract submission is open now, and will close 4th September.

This includes classification, regression, likelihood-free inference, anomaly detection, network acceleration, knowledge distillation, uncertainty mitigation, fast simulation, semi-supervised learning, or whatever else you may be working on that is related to ML in HEP. This is your opportunity to share the exciting things that you have been working on with the rest of the LHC community and more!
(Please just make sure that you abide by any experimental privacy constraints. In case you work on one of the four main LHC collaborations and are unsure of what this means, please contact us! We can help you sort it out.)


Best regards,
The IML coordinators
PS : Please do not hesitate to forward this announcement to relevant local/national/institutional mailing lists

Gian Michele Innocenti ALICE
David Rousseau ATLAS
Loukas Gouskos -> Pietro Vischia CMS
TBA LHCb
Lorenzo Moneta CERN-SFT
Riccardo Torre CERN-TH
Andrea Wulzer CERN-TH

❇️ *** Discussion title: Machine Learning

Dear Colleagues,

in the graduate school at the RWTH Aachen University, the Research
Training Group (RTG): Physics of the Heaviest Particles at the LHC

https://www.rwth-aachen.de/rtg2497/

recent job openings for several PhD positions have been published in:
https://labs.inspirehep.net/jobs/1759310

Applications have to be sent to
https://academicjobsonline.org/ajo/jobs/16509 until July 31st, 2020

In particular we are looking for candidates interested in the topic:
“Search for dark matter with machine learning in leptonic channels at
the LHC”

A master in Physics according to the Bologna guidelines, profound
experience in analysis and initial knowledge in machine learning are
prerequisites to perform the PhD.

Please forward this announcement to possible candidates.

Thank you in advance,

Kerstin Borras
(DESY and RWTH Aachen University)