Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Germany
- France
- Sweden
- United Kingdom
- Spain
- Singapore
- Netherlands
- Belgium
- Portugal
- Norway
- Denmark
- Poland
- Austria
- Australia
- Switzerland
- Czech
- Italy
- Canada
- Luxembourg
- Ireland
- United Arab Emirates
- Finland
- Hong Kong
- Morocco
- Cyprus
- Brazil
- Romania
- China
- Bulgaria
- Latvia
- Japan
- Lithuania
- New Zealand
- Croatia
- Estonia
- Greece
- Kuwait
- Malta
- Slovakia
- Andorra
- Europe
- India
- Mexico
- Slovenia
- Worldwide
- 36 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Biology
- Medical Sciences
- Economics
- Science
- Mathematics
- Materials Science
- Business
- Chemistry
- Environment
- Earth Sciences
- Psychology
- Humanities
- Linguistics
- Electrical Engineering
- Arts and Literature
- Education
- Physics
- Social Sciences
- Law
- Philosophy
- Sports and Recreation
- Statistics
- 14 more »
- « less
-
acquisition, calibration, uncertainty/rigour and noise control). Strong signal processing skills (e.g., using Python and/or MATLAB). Ability to work effectively in an interdisciplinary team and communicate
-
models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
-
vision and deep learning concepts, including object detection and image-based classification, with hands-on experience using Python and at least one deep learning framework (e.g., PyTorch or TensorFlow
-
: https://www.slu.se/institutioner/vaxtbiologi-skogsgenetik/ Read more about our benefits and what it is like to work at SLU: https://www.slu.se/om-slu/jobba-pa-slu/ PhD Student: DDLS integrative
-
experiments using high-level programming languages (e.g., Python, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including parameter estimation and uncertainty
-
evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference - implementation of your machine learning pipeline in Python (using e.g
-
Offer Description Scope of work The successful PhD candidates will be part of the group created by dr Jacek Herbrych (https://jacekherbrych.github.io ) within project NCN SONATA BIS 13 2023/50/E/ST3/00033
-
into giving trends. Ideally utilizes R or Python to implement internal models as well as manage external models Identify and qualify new prospects through routine screenings and employment of electronic
-
ability with Python, and ability to learn new languages and programming paradigms Demonstrated ability to develop new code and analyses in the context of a research project Equivalent education and/or
-
Analyses and Machine Learning (Python) Feature extraction from physiological data (signal processing) Development and validation of machine learning models Implementation of reproducible analysis pipelines