Join us!
Position Title: Researcher in Archaeological Materials and Machine Learning
Duration: 1-Month Pilot Project (with potential for extension)
Location:[Remote]
Project Overview
We are launching a pilot study to explore the use of machine learning in archaeological materials, focusing on metal and slag composition data to predict time period, smelting style, bibliometric references, comparative/parallel data and technological signatures. This project combines archaeometallurgy, data science, and digital humanities to test how computational methods can enhance the interpretation of archaeometric datasets.
Responsibilities
- Compile and curate standardised datasets of metal and slag compositional analyses (XRF, ICP-MS, SEM-EDS, etc.).
- Apply machine learning techniques (classification, clustering, dimensionality reduction) to predict time period and smelting technology based on chemical composition.
- Conduct bibliometric searches and database mining of published archaeometallurgical studies to build a comparative reference framework.
- Integrate archaeological context with computational results to support interpretative outcomes.
- Produce a pilot report summarising methods, results, and recommendations for scaling up.
- Built an accessible, user-friendly interface.
- Deliver admin rights for data uploading and monitoring for wide groups in a standardised format, including techniques, normalisation/unnormalisation features, etc and optimum graphing solutions.
Requirements
- Knowledge of datasets, vocabularies and contextual references. Our team will liaise with the candidate and support them with this.
- Experience with machine learning tools (e.g., Python: scikit-learn, XGBoost, TensorFlow, or equivalent).
- Skills in data cleaning, normalisation, and visualisation of compositional datasets.
- Familiarity with bibliometric methods and literature databases (e.g., Web of Science, Scopus, Google Scholar)
Desirable (not mandatory)
- Experience with compositional databases in archaeology (e.g., archaeometallurgical or ceramic reference datasets).
- Knowledge of smelting technologies and slag typologies.
- Interest in digital archaeology and data-driven approaches.
What We Offer
- Opportunity to contribute to a pioneering pilot project at the intersection of archaeology and machine learning.
- Interdisciplinary collaboration with archaeologists, data scientists, and heritage researchers.
- Potential for extended involvement in larger-scale projects and publications.
Application
Send us your CV and any online design portfolio with a subject line: ‘Application: Internship -ArcML’ to career@cityai.space
Position: Web Developer (Short-Term Contract)
Project: IAMS Website Development
The Institute for Archaeometallurgy and Materials (IAMS) is seeking a web developer to build a new website to host project information, research updates, and a current job description/advert. The site should also support a IAMS bibliometric and comparative database with search and filtering features.
Goal
Build a clean, responsive website for IAMS that presents the project, hosts the 1-month pilot job advert, and provides core research features: a searchable IAMS bibliometric database, projects/publications pages, contact & application forms, and admin-friendly content editing.
Key Tasks
- Develop a clean, responsive website (WordPress or equivalent CMS).
- Implement pages for About, Research/Projects, Jobs, and Contact.
- Set up a bibliometric/comparative database with import (CSV) and search/filter functions.
- Ensure the site is user-friendly, accessible, and easy to maintain.
Requirements
- Proven experience in website development and CMS integration.
- Skills in database search/filter implementation.
- Ability to deliver within a short timeline (approx. 1 month).
Send us your CV and any online design portfolio with a subject line: ‘Application: Internship -WebIAMS’ to career@cityai.space