Based on the provided job description, compose a brief and impactful cover letter. The letter should be formatted into three paragraphs. Begin with succinct job name that you want to apply. Then highlight motivation for this particular position. Finally, emphasis why you should be accepted through incorporating relevant projects. You can relate project descriptions from the provided resume with the position, underpinned by tangible examples why and how they tailored to the position. You can mimick how axample cover letter is written for the example job description. The letter should be written in a professional, yet conversational tone. Avoid using any placeholders, and ensure that the letter flows naturally and is tailored to the job.

Please provide only the text of the cover letter. Do not include any header, footer, introductions, explanations, or additional information.

## Job Description:
```
In an era of rapid advancements in Artificial Intelligence (AI) applied to various geo-applications, the requirement of responsible GeoAI has become more and more critical to cope with issues, such as bias, uncertainty, and lack of explainability.

This project will provide you with the opportunity to investigate functional responsibility, particularly investigating biases, e.g., imbalance in data and spurious correlations, in the processes of deep learning vision-based GeoAI applications, such as classification, semantic segmentation, and structure prediction using aerial imagery.

You will be at the forefront of exploring and developing brand-new approaches to addressing bias in deep learning-powered models and contributing to the emerging research area in Earth Observation. Your research includes, e.g., detecting representation bias in (benchmark) datasets, supervising algorithmic bias in modelling, evaluating the impact of bias on prediction results, and ultimately mitigating bias in different learning stages.

You will present (both preliminary and mature) results at appropriate meetings and conferences, publish results in scientific workshops, conference proceedings, and journals, contribute to teaching on topics related to your work, and supervise and mentor involved MSc students as needed.

We invite applications for this PhD position at the Earth Observation Science (EOS) Department at the ITC Faculty of Geo-information Science and Earth Observation. We are looking forward to a highly motivated and talented candidate to work on this PhD project at the intersection of Computer Vision and Earth Observation Science.
```
## My resume:
```
personal_information=PersonalInformation(name='Federico', surname='Elia', date_of_birth='10/04/2001', country='Ireland', city='Dublin', address='Dublin 1', zip_code='520123', phone_prefix='+353', phone='892338335', <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>', github=Url('https://github.com/feder-cr'), linkedin=Url(<PRESIDIO_ANONYMIZED_URL>)) education_details=None experience_details=None projects=[Project(name='Prompt-Guided Consistency Learning for Multi-Label Classification with Incomplete Labels', description='Under review at journal Neural Networks, 2024. Abstract: Addressing insufficient supervision and improving model generalization are essential for multi-label classification with incomplete annotations, i.e., partial and single positive labels. Recent studies incorporate pseudo-labels to offer additional supervision for the model and enhance its generalization capabilities. However, the noise in the pseudo-labels self-generated by the model keeps accumulating, leading to confirmation bias in the training process. As an effective approach for few-label learning, consistency regularization tries to prevent the accumulation of pseudo-label noise through data-augmented diversity, but it is based on model predictions only to provide limited mitigation of confirmation bias. To further mitigate confirmation bias, we build a prompt-guide consistency learning (PGCL) framework for two incomplete labeling settings. Specifically, we propose intra-category supervised contrastive loss to impose the consistency constraint in per-category feature space, instead of spanning across the feature spaces of all categories as in traditional inter-category supervised contrastive loss. Besides, we design a class-specific semantic decoupling module, leveraging the strong vision-language alignment capability of CLIP, since the proposed contrastive loss requires high-quality label-level representations as contrastive samples. Extensive experimental results on multiple datasets show that our method can effectively solve the problem of two incomplete labeling settings and achieve state-of-the-art performance.', link=None), Project(name='A Benchmark and Baseline for Open-Set Incremental Object Detection', description='Published at International Joint Conference on Neural Networks (IJCNN), 2024. Abstract: In real-world applications, detectors are expected to evolve and improve their perceptual abilities through incremental learning of the unknown. Open world object detection (OWOD) simulates this process by recognizing unknown objects and incrementally learning these unknown classes as labeled data becomes available. However, two issues exist in the training images of OWOD datasets: instances of both 1) unknown classes and 2) previously known classes are present. To solve them, this paper proposes Open-Set Incremental Object Detection (OSIOD), which is defined as two processes: learning from the known, and an infinite cyclical process consisting of detection of the known and the unknown, together with incremental learning of the unknown. We construct a benchmark dataset by filtering out images containing unknown and/or previously known instances, which can also be used to create a new incremental object detection scenario where images with only new classes are used for incremental learning. Additionally, we propose a baseline method for OSIOD by introducing label smoothing and fine-tuning for unknown class discovery and catastrophic forgetting, respectively. Comprehensive experiments provide insights into the baseline. We hope that our benchmark, baseline, and insights will promote research towards OSIOD.', link=None), Project(name='A Simple Unknown-Instance-Aware Framework for Open-Set Object Detection', description='Published at international Conference on Information Science and Technology (ICIST), 2023. Abstract: Open-set object detection aims to detect training set objects (known classes) and non-training set objects (unknown classes). The current frameworks rely on human-in-the-loop, i.e., humans provide training data of unknown classes, to be aware of unknown-instance. However, it is difficult to collect unknown-class images in the real world. In this work, we propose a simple unknown-instance-aware framework for open-set object detection, which can extend close-set detectors to open-set object detection. The proposed framework consists of a class-agnostic proposal module and a prompt-guided semantic module. The class-agnostic proposal module generates region proposals, which are used by the prompt-guided semantic module. The prompt-guided semantic module distinguishes unknown instances by the similarity score between embeddings and image features, which is based on the aligned image-text space of pre-trained vision-language models, e.g., CLIP. The experiments show the class-agnostic proposal module is better than the class-aware locator in localization performance, and the prompt-guided semantic module has better unknown-class recognition performance.', link=None), Project(name='Saliency Regularization for Self-Training with Partial Annotations', description='Published at nternational Conference on Computer Vision (ICCV), 2023. Abstract: Partially annotated images are easy to obtain in multilabel classification. However, unknown labels in partially annotated images exacerbate the positive-negative imbalance inherent in multi-label classification, which affects supervised learning of known labels. Most current methods require sufficient image annotations, and do not focus on the imbalance of the labels in the supervised training phase. In this paper, we propose saliency regularization (SR) for a novel self-training framework. In particular, we model saliency on the class-specific maps, and strengthen the saliency of object regions corresponding to the present labels. Besides, we introduce consistency regularization to mine unlabeled information to complement unknown labels with the help of SR. It is verified to alleviate the negative dominance caused by the imbalance, and achieve state-ofthe-art performance on Pascal VOC 2007, MS-COCO, VG200, and OpenImages V3.', link=None), Project(name='Coarse-to-Fine Incremental Few-Shot Learning', description='Published at European Conference on Computer Vision (ECCV), 2022. Abstract: Different from fine-tuning models pre-trained on a large-scale dataset of preset classes, class-incremental learning (CIL) aims to recognize novel classes over time without forgetting pre-trained classes. However, a given model will be challenged by test images with finer-grained classes, e.g., a basenji is at most recognized as a dog. Such images form a new training set (i.e., support set) so that the incremental model is hoped to recognize a basenji (i.e., query) as a basenji next time. This paper formulates such a hybrid natural problem of coarse-to-fine few-shot (C2FS) recognition as a CIL problem named C2FSCIL, and proposes a simple, effective, and theoretically-sound strategy Knowe: to learn, freeze, and normalize a classifier’s weights from fine labels, once learning an embedding space contrastively from coarse labels. Besides, as CIL aims at a stability-plasticity balance, new overall performance metrics are proposed. In that sense, on CIFAR-100, BREEDS, and tieredImageNet, Knowe outperforms all recent relevant CIL or FSCIL methods.', link=None)] achievements=[Achievement(name='Excellence award of the 2nd A Competition', description='Texts as images to tuning the vision-language model CLIP for zero-shot multi-label classification'), Achievement(name='Third place of the 4th B Challenge', description='Open-vocabulary object detection using image-level supervision in addition to the detection data'), Achievement(name='Excellence award of the 3rd C Competition', description='Multiple object tracking based on YOLOv5 and SORT')] certifications=None languages=None interests=None
```
## Example job Description:
```
We are looking for a highly motivated and ambitious PhD candidate to join our team and contribute to cuttingedge research in machine learning and deep learning. This is a fully funded PhD position within the Division of Computational Science and Technology at KTH. Successful candidates will also become part of the SciLifeLab community, Sweden’s leading research infrastructure for life sciences.
As a PhD student, you will work on developing innovative machine-learning and deep-learning frameworks to enhance our understanding and prediction of drug interactions and adverse reactions using diverse data types, including Sweden's comprehensive drug interaction dataset. Your work will focus on the following areas:
Graph Convolutional Networks
Graph Embedding
Multimodal Learning
If you are passionate about advancing AI in pharmacology and eager to work in a collaborative and interdisciplinary environment, we encourage you to apply and join us in addressing these exciting challenges.

```
## Example cover letter:
```
I am writing with great enthusiasm to express my interest in this position - Machine Learning for Drug Interaction Prediction. \\

\textbf{Motivation for this particular position}. This position aligns with my passion, as it offers me a genuine opportunity to apply deep learning technologies in a realistic setting and has the potential to assist researchers. Furthermore, I am interested in developing models that can synthesise information from different modalities, leading to more accurate predictions and a deeper understanding of complex biological phenomena. It resonates with my technical skills and experience of exploring the fusion of images and texts. \\

\textbf{Relevant work}. My relevant work is primarily on multimodal learning, especially my work with the contrastive language-image pre-training (CLIP) model. Like CLIP learns to associate images with textual descriptions, similar techniques could be applied to correlate chemical structures, clinical trial results, and genomic data, offering a more holistic view of drug interaction. By aligning different modalities, we can create a more comprehensive dataset that captures the complexity of drug interactions. Besides, CLIP's cross-modal alignment capabilities can be used to align textual data from clinical notes and research papers with visual data from drug compound images and cellular response images. This alignment can reveal correlations and patterns that are not evident when analyzing data in isolation.

```