Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- Netherlands
- France
- Denmark
- Norway
- Spain
- Portugal
- Belgium
- Australia
- United Arab Emirates
- Switzerland
- Poland
- Austria
- Singapore
- China
- Canada
- Hong Kong
- Luxembourg
- Finland
- Vietnam
- Czech
- Ireland
- Morocco
- Estonia
- Romania
- Italy
- India
- Andorra
- Brazil
- Croatia
- Latvia
- Lithuania
- New Zealand
- South Africa
- Cyprus
- Greece
- Slovenia
- Ukraine
- Chile
- Japan
- Armenia
- Bulgaria
- Indonesia
- Israel
- Kenya
- Qatar
- Saudi Arabia
- Taiwan
- Worldwide
- 42 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Economics
- Biology
- Science
- Mathematics
- Chemistry
- Arts and Literature
- Social Sciences
- Business
- Education
- Psychology
- Humanities
- Materials Science
- Earth Sciences
- Electrical Engineering
- Environment
- Linguistics
- Law
- Physics
- Design
- Philosophy
- Sports and Recreation
- Statistics
- 15 more »
- « less
-
descriptors to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and
-
Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through
-
performance data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research
-
descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
-
the majority of immune cell types. The multi-scale computational model integrates mechanistic molecular and cellular-level models with population whole-body models, utilizing machine learning and distributed
-
seminars, MA seminars and/or specialist classes. The selected candidate is expected to teach courses on topics in the field of quantitative finance, machine learning and data science. Courses should be
-
computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
-
), the sorption of PFAS and heavy metals onto natural nanoparticles will be investigated in situ using a dedicated field exposure method developed by our team, complemented by laboratory experiments and machine
-
the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
-
The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have