PhD. Candidate – Machine Learning and Artificial Intelligence for Forensic Non-Human DNA Recognition and Interpretation

2 Apr 2024
Job Information

Organisation/Company

UNIVERSIDAD AUTÓNOMA DE MADRID

Research Field

Computer science » Other

Researcher Profile

First Stage Researcher (R1)

Country

Spain

Application Deadline

30 Apr 2024 - 00:00 (Europe/Madrid)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

37,5

Offer Starting Date

1 Sep 2024

Is the job funded through the EU Research Framework Programme?

HE / MSCA

Reference Number

HORIZON-MSCA-2022-DN-01-01

Marie Curie Grant Agreement Number

101120165

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

This is a job offer for a Doctoral Candidate (DC) for European Training Network “Natural Traces” (Project 101120165, Horizon Europe, see https://cordis.europa.eu/project/id/101120165 ). The DC will be engaged in cutting-edge research in the context of the use of non-human DNA in forensic recognition and interpretation. This field that has seen significant research and debate in recent years. The European Network of Forensic Science Institutes (ENFSI) has published recommendations in favor of a Bayesian decision framework for communicating results to the court, which involves interpreting forensic findings through the lens of likelihood ratios (LR). This approach aims to establish a common methodology across forensic disciplines, fostering a more standardized and quantitative presentation of results to aid fact finders in legal proceedings. Despite the success of LR methodologies in disciplines such as DNA analysis, challenges persist in obtaining adequate LR values for various real-life scenarios found when non-human DNA is involved. Factors like data scarcity, multiple hypotheses, high dimensionality, and mixtures of trace materials makes the general problem challenging. Moreover, artificial intelligence and machine learning offer a great opportunity to effectively extract evidence that will need to be calibrated in the form of a LR for presentation in court. Some members of the network, including the Ph. D. Advisor for this DC, are members of associate members of ENFSI, which will ensure adequate and useful integration of the outcomes of the project for the benefit of European forensic science.

Hypotheses and Plan: The primary objective of this position is to develop and implement the tools necessary for the accurate computation and validation of likelihood ratios across diverse forensic disciplines. This can be achieved using artificial intelligence (AI), machine learning and statistical methods. The DC will collaborate closely with other sites and fellows within the ETN to achieve the following objectives:

Robust Computation of Likelihood Ratios: Develop methodologies for the reliable calculation of likelihood ratios with non-human DNA evidence, ensuring compatibility with published ENFSI Guideline recommendations, and facilitating interpretation within a common forensic context.

Validation Methodology: Establish rigorous validation procedures to assess the readiness of calculated LR values for use in casework, addressing issues such as data quality, model robustness, and statistical significance.

Solutions for Challenging Scenarios: Devise innovative solutions to compute LR values for complex scenarios, including mixtures of biological materials, by leveraging advanced statistical and AI techniques, and collaboration with domain experts.

Expected Progress: The successful candidate will contribute to the advancement of forensic science by achieving the following milestones:

• Development of methods to calculate LR values for non-human DNA biological trace evidence.
• Implementation of validation methodologies to ensure the reliability and validity of computed LR values.
• Collaboration with ETN members to exchange data, simulate cases, propose robust models, and validate findings.
• Dissemination of research outcomes through publications, presentations, and participation in relevant conferences and workshops.

 

Requirements

Research Field

Computer science » Other

Education Level

Master Degree or equivalent

Skills/Qualifications

• It is mandatory that the DC does not hold a doctoral degree at the recruitment date.
• It is mandatory that the DC should have no residency or main activity (work, studies, etc.) in Spain for more than 12 months in the 36 months before the recruitment date.
• Master Degree (or EU-equivalent) in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, Physics or a related field.
• Expertise in Bayesian statistics, likelihood ratio computation and statistical modeling.
• Expertise in Machine Learning and Artificial Intelligence.
• Experience in forensic science research or relevant interdisciplinary projects is desirable.
• Proficiency in programming languages, preferrably Python.
• Strong analytical, problem-solving, and communication skills.
• Ability to work effectively both independently and as part of a multidisciplinary team.

Specific Requirements

Application Procedure: Interested candidates are invited to submit the following documents:

Curriculum Vitae (CV) highlighting relevant academic qualifications, research experience, and publications.

Cover Letter summarizing their interest in the position and outlining their suitability based on the qualifications and responsibilities described.

Contact information for two professional references.

Applications should be sent via email to Dr. Alicia Lozano ([email protected] ) with the subject line: "Application for Natural Traces Horizon Europe - [Your Name]". The application deadline is 30th April 2024.

Languages

ENGLISH

Level

Excellent

Additional Information
Benefits

• Competitive salary commensurate with qualifications and experience.
• Opportunities for professional development and collaboration with leading experts in the field.
• Access to state-of-the-art research facilities and resources.
• Comprehensive benefits package including health insurance, retirement plans, and vacation allowances.

Additional comments

Ending of contract: August 31st, 2027

Work Location(s)

Number of offers available

1

Company/Institute

AUDIAS – Audio, Data Intelligence and Speech. Escuela Politecnica Superior, Universidad Autonoma de Madrid

Country

Spain

City

Madrid

Postal Code

28049

Street

C/ Francisco Tomas y Valiente 11

Where to apply

E-mail

[email protected]

Contact

City

Madrid

Website

http://www.uam.es/ss/Satellite/es/home/

Street

C/ Einstein 3

Postal Code

28049

E-Mail

[email protected]
[email protected]

Phone

+34 91 497 2217 +34 91 497 3142

STATUS: EXPIRED