IIT Mandi Research Associate Job Available – Applications Invited
IIT Mandi Research Associate Job Available – Applications Invited. PhD Jobs. IIT Mandi Careers. Interested and eligible applicants can check out all of the details on the same below:
FOR THE POST OF RESEARCH ASSOCIATE
CSIR-(EMR-II) Research Project
Indian Institute of Technology Mandi
School of Mathematical and Statistical
Advt No: IITM/SRIC/CSIR-NKU(25/0326/23/EMR-II)/Adv01
Application are invited from interested and motivated candidates for the post of Research Associate (RA) in a time bound research project for a temporary period, purely on contractual basis as per the following details.
Job Title: Research Associate (RA)
No. of Post: 01
Project Title: Development of deep learning algorithm to solve partial differential equations describing ecological phenomenon
Project Tenure: 03 Years
Job Description: This is a temporary post, purely on a contractual basis to conduct the research work on the development of deep learning algorithms to solve partial differential equations describing ecological phenomena. The expected skill set of the candidates are mathematical modelling, ordinary and partial differential equations, theoretical PDE, machine learning and deep learning. It is desirable that the candidate has expertise in computer programming/ Python/ MatLab.
- a) The upper age limit for applying for the award of RA shall be 35 years.
- b) The upper age limit is relaxable upto 5 years in the case of applicants belonging to scheduled castes/scheduled tribes, physically handicapped and women applicants whereas 3 years in case of OBC (Non-Creamy Layer candidates).
Selection Procedure: Selection will be made through an interview whereby the assessment of academic record and published/project work is tested by discipline-wise high-level expert Committees.
- a) Ph.D. (in a science or engineering subject) or MD/MS/MDS (in medical science subject) or ME/MTech/MPharm/MVSc with three years R&D experience as on the last date of application, evidenced from RA’ship or associateship or from date of registration of PhD.
- b) PhD (science/engineering) thesis submitted are also eligible for RA’ship. Selection in such cases will be subject to the condition that PhD viva-voce done & declared qualified for award of PhD/awarded PhD degree before the expiry of the validity of the award offer.
- c) The candidate applying for Research Associateship must have at least one research publication in a standard refereed journal as listed in Journal Citation Reports (JCR).
Fellowship: Rs. 47,000/- p.m.
How to Apply:
The candidates possessing the requisite qualification and experience should apply ONLINE through the LINK: https://forms.gle/T3zF1BQueBwyEveJ6
The candidates are advised to upload a soft copy of their CV with all relevant documents
(All certificates and Marks sheet in a single pdf file.
Last date to Apply: 23rd September 2023
PI’s Contact Details:
Dr. Nitu kumari
School of Mathematical and Statistical Sciences,
Indian Institute of Technology Mandi
E-mail Id: [email protected]
Mobile No.: +91-8894303773
Possible Interview Questions and Answers:
- Can you explain your understanding of partial differential equations (PDEs) and their relevance in ecological modeling?
Answer: Partial differential equations are mathematical equations used to describe the behavior of systems that vary continuously in space and time, which is often the case in ecological systems. In ecological modeling, PDEs are essential for simulating complex processes like diffusion, population dynamics, and nutrient transport. They help us understand how ecological variables change over space and time, making them a crucial tool for studying ecological phenomena.
- What motivated you to apply for this Research Associate position and work on this specific project at IIT Mandi?
Answer: I was drawn to this Research Associate position at IIT Mandi because it offers an exciting opportunity to combine my passion for deep learning with ecological research. The project’s focus on developing deep learning algorithms to solve PDEs in the context of ecological phenomena aligns perfectly with my academic background and research interests. I believe this project has the potential to make significant contributions to the field of ecological modeling and sustainability, which is a cause I am deeply committed to.
- Could you provide an overview of your experience with deep learning and its applications in solving scientific problems?
Answer: I have a solid foundation in deep learning, particularly in developing neural network architectures and implementing machine learning algorithms. I have applied deep learning techniques to various scientific problems, including image analysis, natural language processing, and time series forecasting. While my previous work has focused on different domains, the principles of deep learning remain consistent, and I am confident in my ability to adapt these techniques to solve the PDEs relevant to ecological phenomena.
- Have you worked on any similar projects or research that involved interdisciplinary collaboration?
Answer: Yes, I have experience collaborating on interdisciplinary projects. For instance, during my previous role, I collaborated with biologists to develop a deep learning model for classifying and predicting the behavior of animal species based on camera trap images. This required close communication and cooperation between data scientists and ecologists to achieve successful results. I believe my experience in interdisciplinary work will be beneficial for this project.
- Can you discuss any relevant software tools or programming languages you are proficient in for this role?
Answer: I am proficient in several programming languages, including Python, which is widely used in deep learning and scientific computing. I have experience with deep learning frameworks such as TensorFlow and PyTorch, as well as libraries like SciPy and scikit-learn for scientific computing and data analysis. Additionally, I am familiar with version control systems like Git, which are essential for collaborative research projects.
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