[visionlist] Postdoctoral Position (2 years) in Machine Learning, Big Data, and Distributed Computing for Fluorescence Nanoscopy

[Dilip Prasad]

*Postdoctoral Fellow in machine learning, big data, and distributed
computing for fluorescence nanoscopy: *

The Department of Physics and Technology is pleased to announce a vacant
position of a Postdoctoral Research Fellow in the optics group in
connection with the recently funded BioTek 2021 and Nano2021 projects from
Research Council of Norway (RCN). These projects pertain improvement of
throughput of modern nanoscopy approaches, specifically chip-based nanoscopy
<https://www.nature.com/articles/nmeth.4313> and multiple signal
classification algorithm (MUSICAL
<https://www.nature.com/articles/ncomms13752>), and follow up by
application specific big data handling and machine learning such as DoMore
<http://domore.no/>. The appointment is for two years and there is
opportunity to apply for overseas visiting fellowship for 3-12 months in
addition to RCN. The candidate should therefore be open for overseas
fellowship.



The fellow will be a part of the team led by Assoc. Prof. Balpreet Singh
Ahluwalia and Assoc. Prof. Krishna Agarwal. The position pertains employing
of distributed computing, big data, and machine learning for improving the
computational throughput of the nanoscopy by 100 to 1000 times from the
current state, which is based on single thread execution of computational
nanoscopy and absence of data intelligence at present.



The postdoctoral research fellow will have the following goals:

-          Apply distributed computing on MUSICAL to accelerate MUSICAL by
approximately 100 times

-          Design big data handling, storage, and transport approaches for
extremely large nanoscopy images (10 to 100 GBs per image)

-          Design and organize data curation for region-of-interest
segmentation and sample classification in connection to disease and its
severity

-          Design distributed computing machine learning approaches for the
above problems



The project needs the fellow to collaborate actively with biologists having
diverse expertise in order to design biology experiments that can provide
sufficiently valid datasets for supervised learning or unsupervised
learning. In addition the fellow will also have opportunity to work with
other groups members in optics and physics. The fellow will additionally be
required to coordinate with the bioimaging scientists and biologists for
coordinating the data acquisition. Further, being able to communicate
data-centric needs and machine learning parlances in simple terms to
non-experts on the topics, good communication skills and ability to explain
technical things in laymen terms is important. Since the amount of data
required for such problems is huge, distributed computing and big data
expertise is quite critical. The project’s research outcomes will lead to
high impact publications as well as potential commercialization.



*Contact*

For more information about the position and UiT, please contact Assoc.
Prof. Balpreet Singh Ahluwalia Balpreet.singh.ahluwalia at uit.no or Assoc.
Prof. Krishna Agarwal by email krishna.agarwal at uit.no or telephone +47 776
45157. Additionally one can visit group page https://www.3dnanoscopy.com/



*Qualifications*

The position requires a Norwegian doctoral degree in optical microscopy, or
a corresponding foreign doctoral degree recognised as equivalent to a
Norwegian doctoral degree. Priority will be given to candidates who have
completed their doctoral degree no more than five years before the
application deadline unless special circumstances exist. Having a PhD
degree is required before commencement for the position. If you are in the
process of completing your PhD, you must document that you have submitted
your PhD thesis or expected date of Ph.D thesis submission.



The main qualifications and technical *requirements* that fellow should
have are:

•       Prior experience of at least two years in distributed computing,
machine learning, computer vision, and/or big data.

•       Experience in more than two diverse machine learning problems such
as ROI segmentation, classification, object or template detection,
event/anomaly detection in images

•       Publication record illustrating first author publication of
experimental research in tier 1 journals/conferences in the field of
pattern recognition, machine learning, computer vision, distributed
computing, or big data

•       Record of packaging, releasing, or supporting software
packages/source codes for research communities



In addition, it is *desirable* that the candidate demonstrates some of the
below mentioned skills:

•       Prior experience with medical images, microscopy images, or
fluorescence images

•       Exposure to optics, bioimaging, or technopreneurship

•       Established record of collaboration between two or more teams

•       Self-motivation, enthusiasm, and independence

•       Good written and verbal communication skills in English

•       Excellent work ethic and commitment to the job

•       Creativity, ability to think outside the box, problem solving
orientation are extremely desirable

•       Experience in mentorship, leadership, multi-disciplinary research,
and international collaborations are desirable



*Application*

Your application must include:

   - Application letter describing
      - why the candidate considers oneself suitable for the position
      - what motivates the candidate to apply for the position and what the
      candidate expects from this position
   - CV, including the projects undertaken
   - Full list of publications, including software releases. Also
indicate which
   three publications the candidate considers the best output and why.



*Expected start date of the position*: *1st April, 2019*



*Salary*

Remuneration of Postdoctoral Fellow positions are in salary code 1352, and
normally start at salary grade 57 on the pay scale for Norwegian state
employees. There is a 2% deduction for contribution to the Norwegian Public
Service Pension Fund.



*Other information*

*UiT the Arctic University of Norway <https://en.uit.no/startsida>* *with
its main campus located in the quaint little city of Tromsø is the
northernmost university in the world. It houses the optics group
<https://en.uit.no/forskning/forskningsgrupper/gruppe?p_document_id=398136>
in the Department of Physics and Technology, Faculty of Science and
Technology, at the main campus of UiT. The optics group has experienced a
great surge in research, thanks to a constant flow of Horizon2020 funding
through ERC and MSCA-IF projects and funding from research council of
Norway through diverse projects. The group members represent various
disciplines like optics, photonics, fabrication, biology, mathematics,
sensing, microscopy, nanoscopy, chemistry, computer engineering, electronic
instrumentation, etc. The group’s core research activity targets
development of cutting-edge technologies in nano-photonics, optics-based
climate sensing, microscopy, and optical and computational nanoscopy.*



The nature of the project and the job scope is that it allows an all
rounded development of the fellow. The fellow will be encouraged and
supported to build his/her resume towards their ambition in academic,
technopreneurial, or main stream industrial career. The fellowship also
offers opportunity of mentoring PhD students in the team and involvement in
teaching, if it is of interest to the fellow. There is an opportunity for
the fellow to build his/her research network through inheriting the
collaborations of the group. Major collaborators, beyond UiT and directly
relevant to the project, include USoton (UK), NTNU (Norway), UiO (Norway),
IIT (Italy), IFOM (Italy), University of Campagnia (Italy), EMBL (Germany),
NUS (Singapore), ASTAR (Singapore), BU (China), Sun Yat Sen University
(China), and IIT (India). The fellow will also be encouraged to become a
member of Digital Life Norway <https://digitallifenorway.org/> (DLN), a
national platform for multi-disciplinary research with life sciences as a
focus. DLN offers several courses and career development programs, which
the fellow can participate in as a student or an instructor.
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