30 deep-learning "MIT" positions

Student Assistant or Master's thesis Computer Vision / Deep Learning

subject with above-average grades You have very good programming skills in deep learning frameworks and Python Ideally, you already have basic knowledge of computer vision and computer graphics You enjoy

Associate Director, MITS Local Applications

to submitting your application, please review and update (if necessary) the information in your candidate profile as it will transfer to your application. Job Title: Associate Director, MITS Local Applications

Research Engineer Intern - Generative Deep Learning for Protein Design

machine learning solutions to Protein Engineering. You will follow the latest developments in both Deep learning and Structural Biology, to identify and harness the approaches best suited to Protein Design

Zhejiang University (ZJU) Global Recruitment

Basic Requirements We are seeking full-time team members to join us at Zhejiang University (ZJU), ideally those who bring a deep passion for education to their work. Ideal candidates should be in

MODÈLES NEURONAUX AUTO-ORGANISATEURS ET IMPLANTATION MATÉRIELLE POUR L'APPRENTISSAGE CONTINU.

growing neural gas network learns topologies In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 625 632. MIT Press, Cambridge MA, 1995. [3] L

Research Assistant in the Division of Science (Computer Science)

run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures

Technical Associate I

generally and AI in particular. Responsibilities will include analyzing trends in deep learning or other aspects of computing; gathering primary or secondary data; preparing research visualizations; writing

Research assistants - National Centre for Register-based Research, Aarhus University

. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression

Doctoral student (licentiate) in DMDU for Transport Planning

Making Under Deep Uncertainties (DMDU) project aims to shift the paradigm from a traditional 'predict-then-act' model, which optimizes based on a single 'best-guess' future (forecast), to a model that

PhD Research Intern - Machine Learning for Biology

, developing prototype applications and models, implementing software libraries, and writing research papers. Responsibilities Develop novel algorithms and research ideas in deep learning. Contribute to team