51 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"IMEDEA-CSIC-UIB" positions in Spain
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- ICN2
- Nature Careers
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
- Centre for Genomic Regulation
- Computer Vision Center
- Consejo Superior de Investigaciones Científicas (CSIC)
- Institut de Físiques d'Altes Energies (IFAE)
- UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA
- Universitat de Girona
- AMBER laboratory
- Basque Center for Macromolecular Design and Engineering POLYMAT Fundazioa
- CSIC
- Complutense University of Madrid
- Computer Vision Center (CVC)
- Fundació per a la Universitat Oberta de Catalunya
- Hult
- Technical University of Cartagena
- UNIOVI
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- 9 more »
- « less
-
Field
-
to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
-
AI4Science project, specifically focusing on the intersection of advanced machine learning and sustainable catalysis discovery. The primary incentive of this Postdoctoral Fellowship is the chance to contribute
-
Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
-
hypotheses. The candidate will apply machine learning models to clinical and omics data for classification tasks. We are looking for highly motivated and organized candidates with good communication skills
-
Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
-
of artificial intelligence (AI) and biomedical engineering. Research directions include deep learning, natural language processing, brain–computer interfaces, and their applications in disease prediction, drug
-
to provide internal operational guidance and advanced tools that accelerate the adoption and development of machine learning (ML) methods across the centre. Its mission is supported by a set of strategic lines
-
parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
-
assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image
-
valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials