Postdoctoral positions in Physics-based AI at UiT - The Arctic University of Norway
126 views
Skip to first unread message
Deepak Gupta
Jul 24, 2022, 12:23:35 PM7/24/22
to ml-...@googlegroups.com
Dear All,
We are currently looking to fill two postdoctoral positions of 3 years each on the topics of physics-based AI at the Department of Computer Science at UiT - The Arctic University of Norway in Tromso Norway. We will be filling one position in the month and the other within 3 months from now. If you are finishing your PhD with deep learning/computer vision with inclination towards physics modelling, or finishing a PhD in physics with an inclination towards computer vision, feel free to reach out to us.
The positions being announced here relate to Physics-based Artificial Intelligence. The positions entail developing machine learning models towards interpretable and scalable artificial intelligence, and with life science interpretations from 2D/3D microscopy (fluorescently-labeled and label-free) image and video data of biological samples as the target application area. The positions offer a unique opportunity of developing a career in a highly multidisciplinary (artificial intelligence, physics/optics, life science, computational modeling) cutting edge research arena with a potential to create high impact. The candidate will have the opportunity to work across two teams – Bio-AI Lab (https://www.bioailab.org/) and 3Dnanosopy group (https://www.3dnanoscopy.com/) which are both funded through several prestigious EU and Research Council of Norway projects.
Duration: The normal period of appointment is three years. Start date: early 2023.
Location: UiT The Arctic University of Norway, Tromsø, Norway
Salary: The remuneration for Postdoctoral research fellow is in accordance with the State salary scale code 1352. At present gross salary for postdoctoral fellow starts at NOK 553,100 per annum. A compulsory contribution of 2% to the Norwegian Public Service Pension Fund will be deducted.
Related research papers:
- A.A. Sekh, I-S. Opstad, A.B. Birgisdottir, T. Myrmel, B.S. Ahluwalia, K. Agarwal, and D. K. Prasad, “Learning nanoscale motion patterns of vesicles in living cells,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, USA, 14-19 June, 2020.
- A.A. Sekh, I-S. Opstad, G. Godtliebsen, A.B Birgisdottir, B.S. Ahluwalia, K. Agarwal, and D.K. Prasad, “Physics-based machine learning for subcellular segmentation in living cells” Nature Machine Intelligence, 3(12), 1071-1080, 2021.
- Z. Liu, M. Roy, D.K. Prasad, and K. Agarwal, “Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering,” IEEE Transactions on Computational Imaging, 8, 236-245, 2022.
Qualification requirements:
This position requires a PhD degree or equivalent in Computer Science, Mathematics, Computing, or Engineering in topics related to artificial intelligence. The candidate must have had experience with developing, customizing, and applying modern deep learning architecture. Candidate should have a publication and open source code profile related to these topics. Experience of publishing works related to artificial intelligence in top computer science publication venues (CVPR, ECCV, ICCV, NeurIPS, ICML, ICLR, IEEE TIP, IEEE TPAMI, IEEE TCI, IEEE TMI) is preferred. Experience of supervising/mentoring students at bachelor, master, or PhD level is a plus.
The other mandatory requirements are:
Experience of working with computer vision and deep learning toolkits on at least one of the following platforms – Python, C/C++, MATLAB, Keras, PyTorch, Tensor Flow
Demonstration of programming proficiency in at least 2 of the following platforms: Python, MATLAB, OpenCV, Keras/PyTorch/Tensor Flow,etc.
A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, scalable learning, and interpretable learning, artificial intelligence for bioimaging.
Emphasis shall also be attached to personal suitability.
Interested candidates can contact Dr. Dilip Prasad (dilip....@uit.no) and/or Dr. Deepak Gupta (deepak...@uit.no).
We are currently looking to fill two postdoctoral positions of 3 years each on the topics of physics-based AI at the Department of Computer Science at UiT - The Arctic University of Norway in Tromso Norway. We will be filling one position in the month and the other within 3 months from now. If you are finishing your PhD with deep learning/computer vision with inclination towards physics modelling, or finishing a PhD in physics with an inclination towards computer vision, feel free to reach out to us.
The positions being announced here relate to Physics-based Artificial Intelligence. The positions entail developing machine learning models towards interpretable and scalable artificial intelligence, and with life science interpretations from 2D/3D microscopy (fluorescently-labeled and label-free) image and video data of biological samples as the target application area. The positions offer a unique opportunity of developing a career in a highly multidisciplinary (artificial intelligence, physics/optics, life science, computational modeling) cutting edge research arena with a potential to create high impact. The candidate will have the opportunity to work across two teams – Bio-AI Lab (https://www.bioailab.org/) and 3Dnanosopy group (https://www.3dnanoscopy.com/) which are both funded through several prestigious EU and Research Council of Norway projects.
Duration: The normal period of appointment is three years. Start date: early 2023.
Location: UiT The Arctic University of Norway, Tromsø, Norway
Salary: The remuneration for Postdoctoral research fellow is in accordance with the State salary scale code 1352. At present gross salary for postdoctoral fellow starts at NOK 553,100 per annum. A compulsory contribution of 2% to the Norwegian Public Service Pension Fund will be deducted.
Related research papers:
- A.A. Sekh, I-S. Opstad, A.B. Birgisdottir, T. Myrmel, B.S. Ahluwalia, K. Agarwal, and D. K. Prasad, “Learning nanoscale motion patterns of vesicles in living cells,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, USA, 14-19 June, 2020.
- A.A. Sekh, I-S. Opstad, G. Godtliebsen, A.B Birgisdottir, B.S. Ahluwalia, K. Agarwal, and D.K. Prasad, “Physics-based machine learning for subcellular segmentation in living cells” Nature Machine Intelligence, 3(12), 1071-1080, 2021.
- Z. Liu, M. Roy, D.K. Prasad, and K. Agarwal, “Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering,” IEEE Transactions on Computational Imaging, 8, 236-245, 2022.
Qualification requirements:
This position requires a PhD degree or equivalent in Computer Science, Mathematics, Computing, or Engineering in topics related to artificial intelligence. The candidate must have had experience with developing, customizing, and applying modern deep learning architecture. Candidate should have a publication and open source code profile related to these topics. Experience of publishing works related to artificial intelligence in top computer science publication venues (CVPR, ECCV, ICCV, NeurIPS, ICML, ICLR, IEEE TIP, IEEE TPAMI, IEEE TCI, IEEE TMI) is preferred. Experience of supervising/mentoring students at bachelor, master, or PhD level is a plus.
The other mandatory requirements are:
Experience of working with computer vision and deep learning toolkits on at least one of the following platforms – Python, C/C++, MATLAB, Keras, PyTorch, Tensor Flow
Demonstration of programming proficiency in at least 2 of the following platforms: Python, MATLAB, OpenCV, Keras/PyTorch/Tensor Flow,etc.
A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, scalable learning, and interpretable learning, artificial intelligence for bioimaging.
Emphasis shall also be attached to personal suitability.
Interested candidates can contact Dr. Dilip Prasad (dilip....@uit.no) and/or Dr. Deepak Gupta (deepak...@uit.no).
Reply all
Reply to author
Forward
0 new messages