PhD Fellow in Computer Science (Artificial Intelligence)
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Dilip K. Prasad
Sep 18, 2021, 11:07:19 PM9/18/21
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PhD Fellow position on AI with the Department of Computer Science, UiT The Arctic University of Norway.
Deadline - 17th October 2021
Location- Tromsø, Norway
A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, and interpretable learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary.
Applicants must document fluency in English and be able to work in an international environment. Working knowledge of Norwegian or a Scandinavian language is beneficial.
We will also emphasize motivation and personal suitability for the position.
As many as possible should have the opportunity to undertake organized research training. If you already hold a PhD or have equivalent competence, we will not appoint you to this position.
best regards,
We have the following PhD position opening in our bioAI Lab for the RCN project “nanoAI : Nanoscale artificial intelligence in microscopy and nanoscopy for life sciences” during 2021-2025.
Deadline - 17th October 2021
Location- Tromsø, Norway
Remuneration - approx. 48,000/- Euro per annum (Remuneration of the PhD position is in State salary scale code 1017. A compulsory contribution of 2% to the Norwegian Public Service Pension Fund will be deducted.)
Our partners from the Department of Physics & Technology and the Department of Clinical Medicine collaborate to provide raw microscopy data of biological systems, for which the AI solutions will be designed within this project.
The announced PhD position relates to developing computer vision and machine learning models, including interpretable and scalable artificial intelligence, for 3D microscopy (labeled and label-free) image and video data of biological samples such as heart cells and engineered heart tissues and performing AI-based analytics on such data. Interpreting processes inside living systems and label-free images/videos of cells and tissues is a daunting task.
The candidate will work on the problems: Images of biological samples appear as gray scale images devoid of color, texture, and edges. Therefore, they lack features conventionally used in deep models for identification of individual structures. New suitably designed and trained intelligence models have to be developed. If conventional AI approaches such as deep learning and generative adversarial networks are used, large training dataset with correlated image sets of labeled and label-free images are needed, which is a significant challenge. There is a need of new out-of-box AI solutions that derive and improve intelligence, as new data becomes available. 2D and 3D video segmentation, tracking, morphology analysis, graph-based artificial intelligence, time-series analysis, spatio-temporal pattern recognition, etc. will be undertaken. In addition, new AI solutions will be developed and adapted for biological microscopy data analysis problem.
Related research papers:
1. 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.
2. 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 sub-cellular segmentation in living cells,” Nature Machine Intelligence, 2021.
Qualifications
This position requires a Master’s degree or equivalent in Computer Science, Mathematics & Computing, or Engineering. Candidates in the final phase of their Master study may apply. 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, C/C++, MATLAB, OpenCV, etc.
Postgraduate coursework or master thesis strongly related to at least 4 of the following topics:
- Machine learning/deep learning
- Computer vision
- Neural Networks
- Optimization theory/ convex optimization/computational optimization
- Linear algebra
- Statistics/statistical machine learning
- Computational modelling of differential and integral equations
- Data science
- GPU programming
- Topologies and/or graph theory
Our partners from the Department of Physics & Technology and the Department of Clinical Medicine collaborate to provide raw microscopy data of biological systems, for which the AI solutions will be designed within this project.
The announced PhD position relates to developing computer vision and machine learning models, including interpretable and scalable artificial intelligence, for 3D microscopy (labeled and label-free) image and video data of biological samples such as heart cells and engineered heart tissues and performing AI-based analytics on such data. Interpreting processes inside living systems and label-free images/videos of cells and tissues is a daunting task.
The candidate will work on the problems: Images of biological samples appear as gray scale images devoid of color, texture, and edges. Therefore, they lack features conventionally used in deep models for identification of individual structures. New suitably designed and trained intelligence models have to be developed. If conventional AI approaches such as deep learning and generative adversarial networks are used, large training dataset with correlated image sets of labeled and label-free images are needed, which is a significant challenge. There is a need of new out-of-box AI solutions that derive and improve intelligence, as new data becomes available. 2D and 3D video segmentation, tracking, morphology analysis, graph-based artificial intelligence, time-series analysis, spatio-temporal pattern recognition, etc. will be undertaken. In addition, new AI solutions will be developed and adapted for biological microscopy data analysis problem.
Related research papers:
1. 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.
2. 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 sub-cellular segmentation in living cells,” Nature Machine Intelligence, 2021.
Qualifications
This position requires a Master’s degree or equivalent in Computer Science, Mathematics & Computing, or Engineering. Candidates in the final phase of their Master study may apply. 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, C/C++, MATLAB, OpenCV, etc.
Postgraduate coursework or master thesis strongly related to at least 4 of the following topics:
- Machine learning/deep learning
- Computer vision
- Neural Networks
- Optimization theory/ convex optimization/computational optimization
- Linear algebra
- Statistics/statistical machine learning
- Computational modelling of differential and integral equations
- Data science
- GPU programming
- Topologies and/or graph theory
A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, and interpretable learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary.
Applicants must document fluency in English and be able to work in an international environment. Working knowledge of Norwegian or a Scandinavian language is beneficial.
We will also emphasize motivation and personal suitability for the position.
As many as possible should have the opportunity to undertake organized research training. If you already hold a PhD or have equivalent competence, we will not appoint you to this position.
best regards,
Dilip K. Prasad
Associate Professor, Bio-AI Lab,
Department of Computer Science,
UiT The Arctic University of Norway,
Realfagbygget,Hansine Hansens veg 56,
9019 Tromsø, Norway
Web https://www.bioailab.org/
Department of Computer Science,
UiT The Arctic University of Norway,
Realfagbygget,Hansine Hansens veg 56,
9019 Tromsø, Norway
Web https://www.bioailab.org/
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