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HIRE FROM US

Access more than 4000 top candidates seeking new roles in Data Science, Machine Learning, Artificial Intelligence, Quant Research/Finance, Software Engineering, Quantum Computing, UX Research, Professional Writing, and more!

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Our Recruitment Solutions

Whether you are looking to save time and money through our Project-Based Recruitment solution or Traditional Staffing Agency model, our programs and services are designed to help you find ideal candidates faster and more efficiently.

Project-Based Recruitment

For just $5,000, your organization can submit one dataset or challenge per cohort to the Erdős Institute’s Project-Based Recruitment Program. Each project is tackled by teams of talented Ph.D. students and postdoctoral researchers participating in our Data Science, UX Research, or Deep Learning Boot Camps. These individuals are eager to showcase their skills and solve real-world problems that matter to you.

How it works​

  1. Submit a Project
    Provide a dataset or a business challenge for our students to solve. This can include tasks such as data cleaning, visualization, predictive modeling, or optimization challenges tailored to your needs.

  2. Engage with Talent
    Watch as our participants—eager to demonstrate their skills—deliver innovative solutions to your problem. This hands-on experience allows you to see their talent and creativity in action, with no commitment required beyond the project.

  3. Evaluate and Recruit
    Gain access to a pool of motivated, highly trained Ph.D. candidates who are actively seeking to transition into data-driven industry roles. Use the insights from the project to identify top performers and potentially recruit them into your organization.

Traditional Staffing Solution

For just 20% of the first-year base salary, the Erdős Institute’s Traditional Staffing Solution takes the hassle out of hiring. We do the work for you, conducting an on-demand, targeted search across our extensive talent pool of Ph.D. graduates, postdocs, and alumni from our Data Science, UX Research, and Deep Learning Boot Camps.

Our team identifies and pre-screens candidates tailored to your specific needs, ensuring you receive only the most qualified and motivated talent. Whether you're hiring for a technical, analytical, or research-based role, we connect you with individuals who are not only experts in their fields, but also eager to make an impact in your organization.​

Examples of projects from prior cohorts

FALL 2024

TEAM

Data Science Boot Camp

Predicting Problematic Internet Use

Daniel Visscher, Emilie Wiesner, Aaron Weinberg

Internet use has been identified by researchers as having the potential to rise to the level of addiction, with associated increased rates of anxiety and depression. Identifying cases of problematic internet usage currently requires evaluation by an expert, however, which is a significant impediment to screening children and adolescents across society. One potential solution is to rely on data that is more easily and uniformly collected: the kind collected by a family physician, a simple survey, or by a smartwatch. The research question this project sets out to answer is: “Can we predict the level of problematic internet usage exhibited by children and adolescents, based on their physical activity and survey responses?”

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github URL

MAY-SUMMER 2024

TEAM

Deep Learning Boot Camp

RivusVox Editor

Zachary Bezemek,Francesca Balestrieri

RivusVox Editor: the world's first near-live zero-shot adaptive speech editing system

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github URL

MAY-SUMMER 2024

TEAM

Deep Learning Boot Camp

Taxi Demand Forecasting

Ngoc Nguyen, Li Meng, Sriram Raghunath, Nazanin Komeilizadeh, Noah Gillespie, Edward Ramirez

Knowing where to go to find customers is the most important question for taxi drivers and ride hailing networks. If demand for taxis can be reliably predicted in real-time, taxi companies can dispatch drivers in a timely manner and drivers can optimize their route decision to maximize their earnings in a given day. Consequently, customers will likely receive more reliable service with shorter wait time. This project aims to use rich trip-level data from the NYC Taxi and Limousine Commission to construct time-series taxi rides data for 63 taxi zones in Manhattan and forecast demand for rides. We will explore deep learning models for time series, including Multilayer Perceptrons, LSTM, Temporal Graph-based Neural Networks, and compare them with a baseline statistical model ARIMAX.

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github URL

MAY-SUMMER 2024

TEAM

Data Science Boot Camp

Continuous Glucose Monitoring

Daniel Visscher,Margaret Swerdloff,Noah Gillespie,S. C. Park,oladimeji olaluwoye

The idea of the project is to predict high glucose spikes from continuous glucose data, smartwatch data, food logs, and glycemic index. The dataset consists of the following:
1) Tri-axial accelerometer data (movement in subject)
2) Blood volume pulse
3) Intestinal glucose concentration
4) Electrodermal activity
5) Heart rate
6) IBI (interbeat interval)
7) Skin temperature
8) Food log
Data is public in: https://physionet.org/content/big-ideas-glycemic-wearable/1.1.2/#files-panel

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github URL

SPRING 2024

TEAM

Data Science Boot Camp

Aware NLP Project III

Mohammad Nooranidoost, Baian Liu, Craig Franze, Mustafa Anıl Tokmak, Himanshu Raj, Peter Williams

This project involves the investigation and evaluation of different methodologies for retrieval for use in RAG (Retrieval-Augmented Generation) systems. In particular, this project investigates retrieval quality for information downloaded from employee subreddits. We investigated the impacts of using clustering, multi-vector indexing, and multi-querying in advanced retrieval methodologies against baseline naive retrieval.

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github URL

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