Research Assistant/Associate for AI-Assisted Microscopy [Closed]
Please prepare a detailed CV AND cover letter (REQUIRED) as a single pdf and upload it in [Form closed]. APPLICATIONS WITHOUT COVER LETTER WILL NOT BE EVALUATED.
The position is open until it is fulfilled; we will shortlist and interview potential candidates every week starting from May 2, 2022, until the position is fulfilled.
We are looking for a Research Assistant to work on a research project funded by Lacuna Fund (With funding from Wellcome Trust). This is a collaborative project with Kathmandu Institute of Applied Sciences (KIAS), where we will create a large public annotated dataset to enable automatic detection of Giardia and Cryptosporidium parasites in smartphone microscope images of water, vegetables, and human stool samples. We will further benchmark state-of-the-art (SOTA) deep learning-based object detection methods and explore further novel methods to improve SOTA. The successful candidate will build upon our ICLR workshop paper, Nakarmi et al., and its follow-up journal version (in preparation). With our revised data collection protocol on whole slide imaging and a larger number of data, the candidate will have an opportunity to experience firsthand real-world problem solving with AI right from data collection.
The project is expected to yield high quality research publications with both application novelty (new smartphone AI assisted microscopes) and methodological novelty (small object detection in noisy low-cost microscopes).
You will work with:
- Eager to learn, hardworking attitude, curious mind and sincere.
- Fluency in Python and Python environment for scientific computing and machine learning particularly numpy and scikit-learn.
- Fluent in one or more of the popular deep learning frameworks such as Pytorch or Tensorflow.
- Experience in training convolutional neural networks with reasonable understanding of basics such as why CNNs are common rather than fully connected networks, regularizations, overfitting and bias vs variance tradeoff.
- Good performance in relevant courses such as linear algebra, computer vision, machine learning, image processing and statistics.
- Good proficiency in communicating methods and results of experiments.
- Experience in state-of-the-art object detectors such as region proposal networks, single stage vs two-stage networks.
- Knowledge of the importance of regularizations and be well aware of key challenges such as generalization and explainability.
- Good skills in scientific writing in English and in visualizing experimental results with graphs and figures.
- Experience in git version control.
- Implement or adapt existing pipeline and basic software platforms to collect microscopic imaging data.
- Implement existing SOTA object detectors for in house microscopic images dataset.
- Identify and document key challenges in using existing models for low-cost smartphone based microscopic images.
- Draft research paper under the guidance of the supervisors on the performance of deep learning object detectors, and its role in improving efficacy of smartphone-based microscopes.
- Explore novel and better methods for small object detection that could be implemented in smartphone microscopes.
- Communicate research results to the larger communities through publications in international conferences and journals.
Minimum Required Qualifications
Bachelor in Engineering or Computing Sciences
Employment Duration and Salary
- 15 months
- Full-time position
- Salary: Depending on expertise and experience.
Dr. Bishesh Khanal (Email: firstname.lastname@example.org)