Post Doctoral Research Associate

Title:
Post Doctoral Research Associate

Department:
MT School of Management

Reports To:
Professor MT School of Management

Position Type:
Staff

Position Summary:
The sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory is seeking a highly motivated post-doctoral researcher to join an innovative project at the intersection of high-energy physics, data science, and real-time data processing. The goal of the sPHENIX experiment is to advance the understanding of Quantum Chromodynamics (QCD) by studying the dynamics of heavy quarks, jet substructure, and parton energy loss in high-energy collisions.

The post-doctoral researcher will contribute to developing a novel Large Language Model (LLM) based approach for event data analysis in the sPHENIX data acquisition system. The sPHENIX experiment employs cutting-edge tracking technology, including the Monolithic-Active-Pixel-Sensor (MAPS)-based VerTeX detector (MVTX) and the INTT detector, to perform high-precision vertexing and momentum measurements crucial for identifying heavy-flavor events. However, due to the inherently low probability of heavy quark production in proton-proton collisions, efficient data acquisition and real-time trigger detection are critical for identifying key events, such as those involving bottom quarks (bb).

The post-doctoral candidate will lead the development of a LLM-based model to identify trigger event. This innovative approach leverages the power of machine learning to enhance data filtering and optimize event selection, facilitating the study of rare, high-energy phenomena.

Essential Functions:

• Develop and implement LLM-based models for event analysis and trigger detection in the sPHENIX experiment.
• Collaborate with physicists, data scientists to optimize the data analysis pipeline for efficient event selection, with a focus on quark production.
• Analyze data from high-energy proton-proton and heavy-ion collisions, focusing on heavy quark dynamics, jet substructure, and QCD phenomena.
• Present results in internal meetings, workshops, and international conferences.
• Contribute to publications and reports related to the project’s findings.

Prerequisite Qualifications:

• Ph.D. in Data Science, Computer Science or a related field, with a strong focus on machine learning, artificial intelligence.
• Strong programming skills in Python.
• Experience with Large Language Models (LLMs), deep learning frameworks.
• Excellent problem-solving abilities and a strong interest in interdisciplinary research.
• At the university's discretion, the education and experience prerequisites may be exempted where the candidate can demonstrate to the satisfaction of the university, an equivalent combination of education and experience specifically preparing the candidate for success in the position.

Preferred Qualifications:
Strong programming skills in C, C++, or similar languages.

Bargaining Unit:
AFT-UCAN

Salary Information:
In compliance with the NJ Pay Transparency Law, the negotiated starting annual salary range for this position is $62,232.00 (USD). NJIT considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as, market and organizational considerations when extending an offer. This pay range represents base pay only and excludes any additional items such as incentives, bonuses or other items.

To learn more about the comprehensive benefits NJIT offers for this position, please visit our benefits page https://hr.njit.edu/health-benefit.

FLSA:
Exempt
Full-Time

Special Instructions to Applicants:
Interested candidates should submit the following:

• A cover letter outlining your qualifications and research interests.
• A detailed curriculum vitae (CV).
• Contact information for at least three academic references (optional).

The position will be based at New Jersey Institute of Technology, with potential travel to Brookhaven National Laboratory as needed.

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