Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Nature Careers
- Fundació per a la Universitat Oberta de Catalunya
- Universitat Pompeu Fabra - Department / School of Engineering
- Barcelona Supercomputing Center (BSC)
- ICN2
- Universidad de Alicante
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- Universitat Politècnica de València
- UNIVERSIDAD POLITECNICA DE MADRID
- University of A Coruña
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
- BARCELONA SUPERCOMPUTING CENTER
- CIC energiGUNE
- IRTA
- Agency for Management of University and Research Grants (AGAUR)
- Barcelona Beta Brain Research Center
- Centre for Genomic Regulation
- Complutense University of Madrid
- FUNDACIO INSTITU DE RECERCA EN ENERGIA DE CATALUNYA
- FUNDACIÓN CANARIA PARQUE CIENTÍFICO TECNOLÓGICO
- Fundación Investigación Biomédica del Hospital de la Princesa
- ISGLOBAL
- Institut d'Investgació i Innovació Parc Taulí (I3PT)
- Institute for bioengineering of Catalonia, IBEC
- Instituto de Neurociencias de Alicante, CSIC-UMH
- Polytechnic University of Catalonia
- UNIOVI
- Universidad Carlos III de Madrid
- Universidad de Alcalá
- Universidade de Vigo
- Universitat Oberta de Catalunya (UOC);
- 21 more »
- « less
-
Field
-
the simulation of mechanotransport in 169 patient-specific disc models using Abaqus, with the aim of extracting biomechanical data and mRNA expression profiles. Subsequently, a machine learning analysis will be
-
(Mandatory): Machine Learning Statistical Modeling Data Visualization Academic Writing Languages (Mandatory): English: IELTS 7.0 or higher LanguagesENGLISHLevelExcellent Research FieldComputer science
-
) and satellite platforms, and surface energy balance models will be used to obtain evapotranspiration (ET); computer vision and machine learning techniques will also be used to identify and count fruits
-
learning models that detect fraudulent activities in decentralized finance (DeFi). Your tasks will include preparing datasets, training and evaluating models, documenting results, supporting demos and
-
signals. -Predictive modeling: Operational knowledge of supervised machine learning, cross-validation,data partitioning, and evaluation metrics. -Best practices: Basic use of version control (Git) and
-
pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
-
, to produce synthetic data. Embedding models for wireless channel representation, e.g., transformer-based, leveraging reconstruction or contrastive learning paradigms. Main research field: Telecommunications
-
: Neural networks and machine learning. Algorithm. Professional Experience: In the use of Python (PyTorch, TensorFlow) and C for the development and optimization of deep learning algorithms. Experience in
-
, required to adequately incorporate molecular data, and model regulations of inflammatory and degenerative processes. Available datasets at the molecular level will be incorporated through machine learning
-
techniques, machine learning, and Large Language Models (LLMs). Knowledge in Human-Computer Interaction (HCI) and Usability Engineering. High level of English (minimum B2). LanguagesENGLISHLevelExcellent