Advertisement Number: P.Rect/R&D/2023/25

About the project: Source Apportionment (SA) is the process of discovering the anthropogenic sources of particulate matter (PM) levels and their relative contributions. SA is a vital tool that helps policy makers understand the major pollutants and take appropriate action to curb pollution levels. The proposed project seeks to establish a novel technique called Dynamic Hyper-local Source Apportionment (DHSA) for real-time and cost effective SA. DHSA seeks to use data from Sensor Ambient Air Quality Monitor (SAAQM), such as gas sensors, meteorological sensors and PM sensors and employ machine-learning techniques to convert SAAQM data into SA information.

About the group/team: This is a highly interdisciplinary project with team members from the Computer Science and Engineering and Civil Engineering disciplines. The team has a high degree of specialization in air quality monitoring as well as machine learning/AI.

About our machine learning/AI work: The team, led by Prof. Kar, engages in cutting-edge research in all major areas of machine learning and AI. The team has published over 40 papers in leading ML/AI conferences and journals such as ICML, NeurIPS, AAAI, IJCAI, CVPR, KDD, etc. These works have received several awards such as best paper awards and have often led to the creation of technologies that impact real-life use. For instance, the team was awarded the Bing Ads Greatness Award (Engineering Excellence category) by Microsoft in 2021 for developing scalable extreme classification techniques for web-scale ranking and recommendation applications. The team has a strong tradition of student-led research and provides a supportive and motivating environment to all its members.

About our air-quality monitoring work: The team has made impactful contributions at the national scale to address the challenges of Air Pollution and Climate Change. Under the leadership of Prof. Tripathi, the team has built ground-breaking innovative approaches for indigenously built sensor-based network technologies for nation-wide urban air quality monitoring and Real Time Source Apportionment (RTSA). Recently, the group has been awarded the International Centre of Excellence on advanced pollution monitoring technologies recognized by the Office of the Principal Scientific Advisor to the Government of India. This work will be carried out as a part of this Centre. RTSA work has been done in Delhi withsupport from CPCB and is currently being carried out with support from Swiss Development Corporation in the cities of Lucknow and Pune.The group has also established sensor networks in 5 cities (Jaipur, Chennai, Guwahati, Kanyakumari and Delhi) with support from Ericsson.

About the position

We are looking for One Project Post-Doc who would be in-charge of data analysis and building machine learning models for sensor calibration and converting SAAQM data to SA insights.

Responsibilities: The candidate would be responsible for taking data generated by multiple SAAQM sensor packages and regulatory grade equipment and using data analysis and ML/AI techniques to generate insights into the data such as outliers, trends, latent correlations. The candidate will be involved in research on developing new machine learning models that can take SAAQM data and convert it to SA insights.

Relevant skills & experience

• Strong background in machine learning, data science and artificial intelligence with fluency in basic ML models such as regression, classification, clustering, component analysis, factor analysis models.
• High ethical standards when performing data analysis and reporting results.
• Willingness to work in an interdisciplinary setting and collaborate closely with team members from other fields, learn about new technologies used in those domains and incorporate this domain knowledge to create more powerful machine learning models.
• Fluency in Python coding and use of ML libraries such as numpy, scipy, sklearn.
• Prior experience with deep learning libraries such as kerasortensor flow.
• Significant background in handling and processing large data sets.
• Impressive publication record in high-impact ML/AI conferences and/or journals.
• Familiarity with advanced topics such as time series models (VAR, VARIMA and deep learning variants such as LSTM, GRU, Transformers) and Gaussian processes will be a plus but is not essential.
• Past background in interdisciplinary research and working with raw data e.g., from instruments or sensors will be a plus but is not essential.

Compensation & benefits

The candidate will get the opportunity to work in a highly motivating and supportive environment. The candidate will be able to avail hostel/family accommodation facilities at IIT Kanpur (according to availability). Adequate support for travel and living costs will be provided (per diem etc.) in case travel or field trips are required. With regards to the medical facilities, only OPD facility for self shall be available at the Health Centre of IIT Kanpur without any reimbursement of cost of medicines. Any outside referral shall not be reimbursed. For the purpose, a medical booklet shall be provided to the candidate on request.

Essential Qualification

• Ph.D. in Computer Science /Electrical Engineering or closely related fields with specialization in Machine Learning/ Artificial Intelligence.

Duration of Appointment 

• The post is purely temporary and is on contractual basis.
• The appointment for the post is up to two years. Appointment will be initially for one year. Renewal of appointment will be subjected to the performance of the candidate.
• Salary: Rs. 65,000/70,000/75,000 per month (as per experience)

The departments reserve the right to fix suitable criteria for short listing of eligible candidates satisfying qualifications and experience. The selection will be based on interviews: online or in person. Short listed candidates will be informed via email about the date of interview. 

Interested candidates may apply via email (to purushot@cse.iitk.ac.in with a copy to dstccp@gmail.com) giving full details qualifications, experience and two recommendation letters with copies of relevant certificates by Feb 16, 2023.The Selection will be based on Zoom/Skype online interview. Shortlisted candidates will be informed of the date of interview by email.