Name of Host Organization

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa

Department or Lab

MagIC (Information Management Research Center) - the NOVA IMS research and development center

Name of Building

Manuel Vilares Building

Physical Address

Campus de Campolide, 1070-312 Lisboa

Website Links

https://www.novaims.unl.pt/

Institutional contact name

Cristina Oliveira

Institutional contact email

magic@novaims.unl.pt

Principal Investigator Name

Professor Ian James Scott

Position / institutional role

Assistant Professor

ORCID

0000-0001-9699-4473

Email

iscott@novaims.unl.pt

TestBed Responsible Name
(if different from PI)

Funding source(s)
for TestBed’s acquisition

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application case:

Short description:

Predictive Maintenance in Manufacturing

Train and deploy machine learning models on sensor and operational data to predict equipment failures before they occur, reducing unplanned downtime, maintenance costs, and workplace safety risks in industrial environments.

Customer Churn Prediction

Develop classification models to identify customers at high risk of churning, enabling businesses to proactively deploy targeted retention strategies and improve customer lifetime value.

Credit Risk Scoring and Financial Fraud Detection

Build gradient boosting and ensemble models for credit risk assessment and real-time fraud detection, leveraging structured transaction data to support safer and more equitable financial decision-making.

Healthcare Outcome Prediction

Apply supervised machine learning to clinical and administrative healthcare data to predict patient readmission risks, disease progression, and treatment outcomes, supporting evidence-based clinical decision-making.

AutoML Pipeline Development and Benchmarking

Use MLLAB infrastructure to systematically benchmark AutoML frameworks across diverse datasets and problem types, supporting research into automated AI democratization tools for non-expert users and resource-constrained organizations.

Time-Series Forecasting for Energy and Demand Planning

Develop and evaluate machine learning models for energy consumption forecasting, demand planning, and anomaly detection in time-series data, supporting sustainable operations and efficient resource management.

Non-academic stakeholders

Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities

Academic stakeholders

MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers

Other types of stakeholders

R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

TRL application range

4

Internal academic research

Yes

Collaborative research with external academic partners

Yes

Contract research / Proof-of-Concept for industry

Yes

Pilot / DeepTech Deployment in operational environment

No

Training services (courses, workshops, certification)

Yes

Service provision (testing, benchmarking, validation)

Yes

Open access for walk-in users (e.g. open days / hackathons)

No

Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)

Yes

Type of contractual relationship

Academic partner

Industrial partner

No contract (direct access)

No

No

Direct contract between parties
(e.g., research agreement)

Yes (See Note 1)

Yes (See Note 1)

Indirect contract between parties
(e.g., project framework)

Yes (See Note 1)

Yes (See Note 1)

Type of prerequisites

Description of prerequisites

Agreements

Confidentiality agreement for proprietary algorithms

In some cases (See Note 2)  

Data sharing agreement for datasets generated

In some cases (See Note 2)

IP agreements

In some cases (See Note 2)

Other 

In some cases (See Note 3)

Insurance

Users must have appropriate liability coverage through their home institution

Yes

Mandatory technical training

N/A

Recommended technical training

Recommended training on cluster operation, job submission, queuing in SLURM.

Mandatory safety requirements

N/A

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Type

Short description:

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH01)

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH02)

Ethical and societal
aspect:

Short description:

Improving Healthcare Outcomes and Reducing Inequalities

Machine learning models trained on clinical data can support earlier and more accurate diagnosis, more personalized treatment recommendations, and better allocation of healthcare resources. For non-technical audiences, this means better chances of early detection of serious illnesses and more equitable access to high-quality medical care regardless of geographic location or socioeconomic status.

Explainability and Human Oversight in AI-Assisted Decisions

As machine learning models are increasingly used to inform consequential decisions affecting individuals' lives, the ability to explain model predictions in human-understandable terms becomes both an ethical imperative and a regulatory requirement. MLLAB integrates explainability tools including SHAP and LIME as standard components of the ML development workflow, supporting the EU AI Act's requirements for transparency and meaningful human oversight of AI-assisted decision-making.

Supporting Safer and More Sustainable Workplaces

Predictive maintenance and anomaly detection models developed on MLLAB infrastructure can identify equipment failures and safety hazards before they cause accidents, directly contributing to worker safety and well-being in manufacturing, energy, and construction sectors, aligned with Industry 5.0 human-centric production principles.