Sort by
Refine Your Search
-
Category
-
Field
-
Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
-
this Ph.D. topic proposal: • The optimal approximation of 3D shapes using meshes is known to be a NP-hard problem. This means that finding the best possible mesh representation for a given shape, while
-
on optimization) and in general be keen on using mathematics to model real problems and get insights. He should also be knowledgeable on machine learning and have good programming skills. Previous experiences with
-
setup, process optimization, and safe, efficient upscaling strategies across various research projects. This position is ideal for someone with a solid understanding of chemistry and polymer science
-
Nature Careers | Port Saint Louis du Rhone, Provence Alpes Cote d Azur | France | about 24 hours ago
research should align with AFMB’s flagship thematic areas: virology and glycobiology. The AFMB Laboratory provides an optimal environment to bridge in silico results with experimental work. Its state
-
suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
-
the world and there is an urgent need to have better prognosis and predictive biomarkers, in order to improve the optimal care of these patients. Many existing therapies lead to an improvement of the overal
-
the optimization and development of new protocols and applications; Prior experience with rodent experimentation, with a Function A / FELASA B certification or equivalent; Experience in services-oriented functions
-
on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
-
of patients treated with immune-checkpoints inhibitors. Our final clinical goals are to help to generate new data-driven tumor response criteria, specifically adapted to immunotherapy, so as to optimize